Direct AI Management of 5G/6G Network Operations

ABSTRACT

Due to the rapid cadence of messages in 5G and expected 6G networks, and rapid variations in the background and interference profile, real-time management decision-making is increasingly impractical for even experienced network operators. Therefore, means are disclosed for AI-based systems to provide support and assistance, including when appropriate to adjust network operational parameters autonomously. After suitable training, processors in a base station, or more preferably a core network facility managing multiple cells, can respond more quickly and more accurately than humans to rapid random changes in demand, interference, intrusion, and emergencies. Disclosed also are means for user devices to keep the base station and the AI management model informed of signal quality upon each uplink message (such as acknowledgements) using vary brief, multiplexed feedback messages responsive to downlink test signals.

PRIORITY CLAIMS AND RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.18/191,017, entitled “AI-Managed Channel Quality Feedback in 5G/6G”,filed Mar. 28, 2023, which claims the benefit of U.S. Provisional PatentApplication Ser. No. 63/327,007, entitled “Modulation IncludingZero-Power States in 5G and 6G”, filed Apr. 4, 2022, and U.S.Provisional Patent Application Ser. No. 63/418,784, entitled“Demodulation for Phase-Noise Mitigation in 5G and 6G”, filed Oct. 24,2022, and U.S. Provisional Patent Application Ser. No. 63/426,853,entitled “Multiplexed Amplitude-Phase Modulation for 5G/6G NoiseMitigation”, filed Nov. 21, 2022, and U.S. Provisional PatentApplication Ser. No. 63/441,488, entitled “Multiplexed Code forACK/SR/Power/Beam Feedback in 5G and 6G”, filed Jan. 27, 2023, and U.S.Provisional Patent Application Ser. No. 63/444,380, entitled “ConciseFeedback for Downlink Beam and Power Adjustment in 5G and 6G”, filedFeb. 9, 2023, and U.S. Provisional Patent Application Ser. No.63/447,167, entitled “Incremental Realtime Signal-Quality Feedback in5G/6G”, filed Feb. 21, 2023, and U.S. Provisional Patent ApplicationSer. No. 63/448,422, entitled “AI-Managed Channel Quality Feedback in5G/6G”, filed Feb. 27, 2023, and U.S. Provisional Patent ApplicationSer. No. 63/451,722, entitled “Lean Deterministic Beam/Power FeedbackDuring 5G/6G Initial Access”, filed Mar. 13, 2023, all of which arehereby incorporated by reference in their entireties.

FIELD OF THE INVENTION

The disclosure pertains to wireless messaging, and more particularly tomethods and formats for providing feedback on signal quality.

BACKGROUND OF THE INVENTION

Wireless messages are required to reach the recipient antenna withsufficient amplitude for reliable reception. Certain messages, such asCSI (channel-state information) messages, can provide some of the neededfeedback, but using bulky complex formats that may be difficult forreduced-capability user devices to manage. What is needed is a compactlow-complexity message format that provides sufficient information tothe transmitter while consuming minimal resources.

This Background is provided to introduce a brief context for the Summaryand Detailed Description that follow. This Background is not intended tobe an aid in determining the scope of the claimed subject matter nor beviewed as limiting the claimed subject matter to implementations thatsolve any or all of the disadvantages or problems presented above.

SUMMARY OF THE INVENTION

In a first aspect, there is a method for a base station of a wirelessnetwork to use an artificial intelligence model to adjust a transmissionbeam parameter, the method comprising: transmitting a downlink messageconcatenated with a first test signal and a second test signal; whereinthe first test signal is transmitted according to a first setting of thetransmission beam parameter, the second test signal is transmittedaccording to a second setting, different from the first setting, of thetransmission beam parameter, and the downlink message is transmittedaccording to a third setting, different from the first and secondsettings, of the transmission beam parameter; receiving, from a userdevice, a feedback message indicating whether the downlink message, orthe first test signal, or the second test signal was best received;providing, as input to the artificial intelligence model, the first,second, and third settings of the transmission beam parameter;providing, as further input to the artificial intelligence model, thefeedback message or a digest thereof; and determining, as output fromthe artificial intelligence model, an adjustment of the transmissionbeam parameter.

In another aspect, there is non-transitory computer-readable mediacontaining an artificial intelligence model and instructions that, whenexecuted by a computing environment, cause a method to be performed, themethod comprising: providing, as input to the artificial intelligencemodel, data about a wireless network, data about a particular userdevice of the wireless network, and data about a background interferencelevel in the wireless network; determining, as output from theartificial intelligence model, a particular setting of a parameter of adownlink transmission beam for communicating with the particular userdevice; and transmitting a downlink message to the particular userdevice, the downlink message transmitted according to the particularsetting of the parameter of the downlink transmission beam.

In another aspect, there is a method for a wireless base station or corenetwork to manage a wireless network, the method comprising: determiningan initial value of a performance metric of the wireless network;providing, as input to an artificial intelligence model, data about thewireless network; providing, as further input to the artificialintelligence model, data about current conditions of the wirelessnetwork; determining, as output from the artificial intelligence model,one or more suggested adjustments of one or more operating parameters ofthe wireless network; adjusting the one or more operating parametersaccording to the one or more suggested adjustments; and determiningwhether the performance metric of the wireless network has increased ordecreased or remained unchanged.

This Summary is provided to introduce a selection of concepts in asimplified form. The concepts are further described in the DetailedDescription section. Elements or steps other than those described inthis Summary are possible, and no element or step is necessarilyrequired. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended foruse as an aid in determining the scope of the claimed subject matter.The claimed subject matter is not limited to implementations that solveany or all disadvantages noted in any part of this disclosure.

These and other embodiments are described in further detail withreference to the figures and accompanying detailed description asprovided below.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a schematic showing an exemplary embodiment of a downlinkmessage with beam test signals, according to some embodiments.

FIG. 1B is a schematic showing an exemplary embodiment of a downlinkmessage with beam test signals configured as demodulation references,according to some embodiments.

FIG. 1C is a schematic showing an exemplary embodiment of a feedbackmessage, according to some embodiments.

FIG. 1D is a schematic showing another exemplary embodiment of afeedback message, according to some embodiments.

FIG. 1E is a schematic showing an exemplary embodiment of a resourcegrid with test signals, according to some embodiments.

FIG. 2 is a flowchart showing an exemplary embodiment of a method forproviding feedback to a base station, according to some embodiments.

FIG. 3 is a schematic showing an exemplary embodiment of an AI structurebased on a neural net, according to some embodiments.

FIG. 4A is a flowchart showing an exemplary embodiment of a method forproviding signal quality input parameters to an AI model, according tosome embodiments.

FIG. 4B is a flowchart showing an exemplary embodiment of a method fordetermining the signal quality of the received test signals, accordingto some embodiments.

FIG. 5A is a schematic showing an exemplary embodiment of a beam-testsignal, according to some embodiments.

FIG. 5B is a schematic showing an exemplary embodiment of an iterativeadjustment of beam angle, according to some embodiments.

FIG. 5C is a schematic showing an exemplary embodiment of an iterativeadjustment of transmission power, according to some embodiments.

FIG. 6A is a flowchart showing an exemplary embodiment of a method foriteratively adjusting a transmission parameter, according to someembodiments.

FIG. 6B is a flowchart showing an exemplary embodiment of a method forproviding iterative feedback, according to some embodiments.

FIG. 7A is a flowchart showing an exemplary embodiment of a procedurefor operating an AI model, according to some embodiments.

FIG. 7B is a flowchart showing an exemplary embodiment of a procedurefor preparing an AI model, according to some embodiments.

FIG. 8A is a schematic showing an exemplary embodiment of a 16QAMmodulation constellation chart, according to prior art.

FIG. 8B is a schematic showing an exemplary embodiment of a QPSKmodulation constellation chart, according to prior art.

FIG. 9A is a schematic showing an exemplary embodiment of a 9QAMconstellation chart, according to some embodiments.

FIG. 9B is a schematic showing an exemplary embodiment of a 8QAMconstellation chart, according to some embodiments.

FIG. 10A is a schematic showing an exemplary embodiment of a polar plotof an amplitude-phase modulation scheme, according to some embodiments.

FIG. 10B is a schematic showing an exemplary embodiment of a polar plotof an 8PSK modulation scheme with a zero-power state, according to someembodiments.

FIG. 11A is a constellation chart showing an exemplary embodiment of anasymmetric QAM modulation scheme, according to some embodiments.

FIG. 11B is a constellation chart showing an exemplary embodiment ofanother asymmetric QAM modulation scheme, according to some embodiments.

FIG. 12A is a polar plot showing an exemplary embodiment of anamplitude-phase modulation scheme with 9 states, according to someembodiments.

FIG. 12B is a modulation table showing an exemplary embodiment of anamplitude-phase modulation scheme with 9 states, according to someembodiments.

FIG. 13A is a polar plot showing an exemplary embodiment of anamplitude-phase modulation scheme with 17 states, according to someembodiments.

FIG. 13B is a modulation table showing an exemplary embodiment of anamplitude-phase modulation scheme with 17 states, according to someembodiments.

FIG. 14A is a chart showing an exemplary embodiment of a inputparameters and output modulation choices of an AI model, according tosome embodiments.

FIG. 14B is a flowchart showing an exemplary embodiment of a method forselecting a modulation type using AI, according to some embodiments.

FIG. 15A is a schematic showing an exemplary embodiment of a systeminformation message including multiple angular transmissions, accordingto some embodiments.

FIG. 15B is a flowchart showing an exemplary embodiment of a procedurefor aligning beams prior to initial access, according to someembodiments.

FIG. 15C is a schematic showing another exemplary embodiment of a systeminformation message including multiple angular transmissions, accordingto some embodiments.

FIG. 16A is a schematic showing an exemplary embodiment of messages forinitial access, according to some embodiments.

FIG. 16B is a schematic showing an exemplary embodiment of messages fora user-initiated beam and power adjustment procedure, according to someembodiments.

FIG. 17A is a schematic showing an exemplary embodiment of messages forinitial access with user beam alignment, according to some embodiments.

FIG. 17B is a schematic showing an exemplary embodiment of messages fora base-initiated beam and power adjustment procedure, according to someembodiments.

FIG. 18A is a schematic showing an exemplary embodiment of threedirectional regions, according to some embodiments.

FIG. 18B is a schematic showing an exemplary embodiment of three widedirectional beams, according to some embodiments.

FIG. 18C is a schematic showing an exemplary embodiment of six regionsdefined by three overlapping beams, according to some embodiments.

FIG. 18D is a schematic showing an exemplary embodiment of threeoverlapping beams that define six regions, according to someembodiments.

FIG. 19A is a schematic showing an exemplary embodiment of eight regionsdefined by four overlapping beams, according to some embodiments.

FIG. 19B is a schematic showing an exemplary embodiment of fouroverlapping beams that define eight regions, according to someembodiments.

FIG. 19C is a schematic showing an exemplary embodiment of angularregions defined by overlapping beams, according to some embodiments.

FIG. 19D is a schematic showing an exemplary embodiment of fouroverlapping beams, according to some embodiments.

FIG. 20A is a schematic showing an exemplary embodiment of a messagewith multiple signals transmitted in different directions, according tosome embodiments.

FIG. 20B is a constellation chart showing an exemplary embodiment of afeedback procedure to select seven or eight angular directions,according to some embodiments.

Like reference numerals refer to like elements throughout.

DETAILED DESCRIPTION

Systems and methods disclosed herein (the “systems” and “methods”, alsooccasionally termed “embodiments” or “arrangements” or “versions” or“examples”, generally according to present principles) can provideurgently needed wireless communication protocols for a user device toprovide feedback messages to its base station based on the receivedpower or signal quality. In versions disclosed herein, the feedbackmessage may be very brief, may include multiple feedback requestsmultiplexed in a terse bit-level code, may be combined with anacknowledgement responsive to a downlink message, and may include ascheduling request with optionally a buffer status report appended. Thedownlink message may include one or more test signals, each test signaltransmitted with different transmission properties such as beam angle.The user device may then transmit the feedback message, indicating thebest received test signal. In addition, the user device may evaluate thesignal quality of the best received test signal, and may include, in thefeedback message, a request for an increase or decrease in transmissionpower according to the received signal quality.

In response to the feedback request, the base station may adjust thetransmission parameter incrementally, that is, by a predetermined smallincrement amount, in the direction requested. For example, the basestation can change the transmitted power or the transmission beam angleby a predetermined positive or negative increment, in the directionrequested by the user device. Such incremental adjustments may be moreefficient and more timely than prior-art messages that attempt tospecify a particular power level or beam angle. At its discretion, thebase station may change the modulation scheme instead of the powerlevel, such as switching to a modulation scheme with fewer modulationlevels or with larger separation between modulation levels. The basestation can create and then update a custom set of transmissionparameters for each user device, thereby accommodating the user device'sreception requirements in realtime.

In addition, user devices in an ad-hoc or sidelink network may providesignal quality feedback to each other using similar test signals andresponsive feedback messages. For example, the user devices can providetest signals on initial contact and upon each sidelink messagethereafter, and can provide responsive feedback messages concatenatedwith an acknowledgement or reply message. For example, sensors andactuators communicating with a manager node or with each other in an IoTsetting, or mobile user devices in a dense urban environment, canmaintain reliable D2D (device-to-device) communications by frequentlymaking incremental adjustments in the transmission parameters (such asfrequency, angle, and power) according to each recipient's feedbackrequests. More specifically, user-A can send a message to user-Bincluding test signals, each test signal transmitted differently. User-Bcan reply with a feedback message indicating which test signal was bestreceived. User-B can also include a second set of test signals, souser-A can select the best one and send another feedback message touser-B, completing the mutual alignment procedure.

The feedback message may be modulated in a compact modulation schemethat does not require amplitude calibration for reliable demodulation.For example, the feedback message may be configured in 9QAM (quadratureamplitude modulation with nine allowed states) of which one state may bea zero-power state. Alternatively, when the zero-power state would beproblematic, the users can use 8QAM which excludes the zero-power state.8/9QAM can provide a tidy modulation scheme for specifying incrementalfeedback choices. The data message and the feedback message may beconfigured as time-spanning (occupying multiple symbol-times at a singlesubcarrier) or frequency-spanning (occupying multiple subcarriers at asingle symbol-time). Examples show how user devices and base stationscan obtain enhanced angular precision using very few test signals.Further examples show multiplexed modulation schemes suitable for thefeedback messages. For example, test signals may be transmitted inpartially overlapping directional beams, thereby enhancing the angularresolution achievable.

Feedback opportunities may be provided with each downlink message byproviding test signals in or with the downlink message. In addition, theinitial access protocol may include multiple opportunities for the userdevice to provide signal quality feedback, thereby assisting the basestation in aligning the downlink transmission beam toward the userdevice. In addition, either the user device or the base station mayrequest an alignment and power adjustment by transmitting a “ping”message to the other entity. Downlink test signals concatenated withdownlink messages, and feedback indicators concatenated with uplinkmessages, may thereby enable highly localized and timely feedback,resulting in reliable communication despite fluctuating backgrounds.

While most of the examples are described for adjusting the downlinkbeam, with the base station transmitting the beam test signals and theuser device providing the feedback message, the reverse is oftenpossible. For example, a user device with beamforming capability canprovide the test signals, and the base station can provide the feedbackmessage, thereby assisting the user device to optimize its uplink beam.As a further option, a base station, or other network asset, maycommunicate wirelessly to other network assets (as in IAB—integratedaccess and backhaul) or with other cells (as in multi-hop transfers andsetup), and thus may use the lean multiplexed feedback proceduresdisclosed herein for improved communication among themselves. Corenetworks, in wireless communication with each other, can include testsignals with backhaul messages, and feedback with reply oracknowledgement messages, so that they maintain sufficient communicationreliability as conditions change.

The base station may determine whether or not the test signals andfeedback responses should be used, depending on the capabilities and QoSrequirements of each user device. Conventions and parameters related tofeedback may be specified in system information messages, or initialaccess messages, or in subsequent uplink or downlink messages such ascontrol messages. For example, mobile user devices that anticipatefrequent changes in beam conditions, may request that test signals beprovided with each downlink message, thereby providing frequent beamtracking while in motion. In other situations, where conditions remainunchanging for extended periods, the user device or the base station maydetermine that the frequent feedback messages are not necessary. The twoentities can then agree to dispense with test signals and feedbackresponses until there is a decline in signal quality. In anotherembodiment, the network may decide to add test signals only to messageslarger than a predetermined size, and not to smaller messages (exceptfor “ping” messages, described later). Alternatively, the test signalsmay be added to messages modulated according to modulation schemescarrying 4 bits per message element or higher, and not to messages withsmaller modulation schemes such as QPSK. In each case, the user devicemay determine, from control messages, whether to expect test signals oneach downlink message.

Artificial intelligence and machine learning may be profitably appliedin several aspects of the feedback process. For example, an artificialintelligence model, or an algorithm derived from it, may be developed toevaluate the signal quality of each received test signal based on theSNR, the received power or amplitude, the measured interference, andother parameters. Further AI models can manage the incrementaladjustment of transmission parameters, such as determining when and byhow much to adjust the beam angle or power in response to a userdevice's feedback. Further AI models may provide assistance indetermining when to change the modulation scheme and to which othermodulation scheme. These are complex multi-parameter problems of thekind that even expert humans generally do poorly, but which awell-trained AI model may provide improved decision-making in a fractionof the time, at negligible cost after installation.

Signal quality feedback, as disclosed herein, may be beneficial in eachwireless communication instance where reliability is important.Transmission beam adaptation using feedback from the recipient may beespecially important when external conditions change, such asatmospheric conditions or encroaching interference. Providingincremental feedback on signal quality upon each downlink message mayprovide enhanced reliability and timely responses to changingconditions, without burdensome signaling and complex encoding. Whenmultiplexed with other requests, such as retransmissions or grantrequests, the cost of feedback may be as low as a few resource elements,with each resource element configured to perform multiple tasks, asdetailed below.

The examples presented herein are suitable for adoption by a wirelessstandards organization. Establishing standards for user devices toconfigure resource-efficient signal-quality feedback messages to thebase station, may thereby optimize communication reliability whilesaving time, resources, and power.

Glossary of Terms

Terms herein generally follow 3GPP (third generation partnershipproject) standards, but with clarification where needed to resolveambiguities. As used herein, “5G” represents fifth-generation, and “6G”sixth-generation, wireless technology in which a network (or cell or LANLocal Area Network or RAN Radio Access Network or the like) may includea base station (or gNB or generation-node-B or eNB or evolution-node-Bor AP Access Point) in signal communication with a plurality of userdevices (or UE or User Equipment or user nodes or terminals or wirelesstransmit-receive units) and operationally connected to a core network(CN) which handles non-radio tasks, such as administration, and isusually connected to a larger network such as the Internet. Thetime-frequency space is generally configured as a “resource grid”including a number of “resource elements”, each resource element being aspecific unit of time termed a “symbol period” or “symbol-time”, and aspecific frequency and bandwidth termed a “subcarrier”. Symbol periodsmay be termed “OFDM symbols” (Orthogonal Frequency-DivisionMultiplexing) in which the individual signals of multiple subcarriersare added in superposition. The time domain may be divided intoten-millisecond frames, one-millisecond subframes, and some number ofslots, each slot including 14 symbol periods. The number of slots persubframe ranges from 1 to 8 depending on the “numerology” selected. Thefrequency axis is divided into “resource blocks” including 12subcarriers, each subcarrier at a slightly different frequency. The“numerology” of a resource grid corresponds to the subcarrier spacing inthe frequency domain. Subcarrier spacings of 15, 30, 60, 120, and 240kHz are defined in various numerologies. Each subcarrier can beindependently modulated to convey message information. Thus a resourceelement, spanning a single symbol period in time and a single subcarrierin frequency, is the smallest unit of a message. “Quadrature” or “QAM”modulation (sometimes “PAM”) refers to two signals, separatelyamplitude-modulated, and then multiplexed and transmitted with a fixed90-degree phase shift between them. The two signals may be called the“I” and “Q” branch signals (for In-phase and Quadrature-phase) or “realand imaginary” among others. Standard modulation schemes in 5G and 6Ginclude BPSK (binary phase-shift keying), QPSK (quad phase-shiftkeying), 16QAM (quadrature amplitude modulation with 16 modulationstates), 64QAM, 256QAM and higher orders. “SNR” (signal-to-noise ratio)and “SINR” (signal-to-interference-and-noise ratio) are usedinterchangeably unless specifically indicated. “RRC” (radio resourcecontrol) is a control-type message from a base station to a user device.“Digitization” refers to repeatedly measuring a waveform using, forexample, a fast ADC (analog-to-digital converter) or the like. An “RFmixer” is a device for multiplying an incoming signal with a localoscillator signal, thereby selecting one component of the incomingsignal. Communications generally proceed in scheduled “channels” such asthe PUSCH and PUCCH (physical uplink shared and control channels) or thePDSCH and PDCCH (physical downlink shared and control channels) inaddition to the PRACH (physical random access channel) and PBCH(physical broadcast channel). System information messages include theSSB (synchronization signal block) and SIB1 (system information blocknumber 1) among others. A BSR (buffer status report) indicates a size ofa planned uplink message. “SR” stands for scheduling request. “CSI”(channel-state information) is a message indicating the signal qualityobtained in a particular downlink channel with a particular beamconfiguration. “CSI-RS” is a test signal usually transmitted by the basestation. “RSRP” (reference signal received power”) is a measure of thesignal quality observed by a recipient. “CSI-RSRP” (channel stateinformation—reference signal received power) is a measure or a messageindicating the signal quality observed by a user device receivingcertain downlink test signals. “SRS” (sounding reference signal) is ameasure or a message indicating the signal quality received by a basestation from a user device. “IAB” or integrated access and backhaulrefers to communication between core networks. “D2D” is device-to-devicecommunication. “AI” is artificial intelligence. “ML” is machinelearning. Other terms may be defined when used.

In addition to the 3GPP terms, the following terms are defined. Althoughin references a modulated resource element of a message may be referredto as a “symbol”, this may be confused with the same term for a timeinterval (“symbol-time”), or a composite waveform or “OFDM symbol”(orthogonal frequency-division multiplexing), among other things.Therefore, each modulated resource element of a message is referred toas a “modulated message resource element”, or more simply as a “messageelement”, in examples below. A “demodulation reference” is one or moremodulated “reference resource elements” or “reference elements”modulated according to the modulation scheme of the message andconfigured to exhibit levels of the modulation scheme (as opposed toconveying data), corresponding roughly to DMRS (demodulation referencesignals) or “pilot” signals of prior art. A “calibration set” is one ormore predetermined amplitude levels and/or phase levels of a modulationscheme, typically determined by a receiver from a demodulationreference. A “short-form” demodulation reference is a demodulationreference that exhibits only selected amplitude levels, such as themaximum and/or minimum amplitude levels, of the modulation scheme, fromwhich the receiver can determine intermediate levels by calculation. Amessage may be transmitted “time-spanning” by occupying successivesymbol-times on a single subcarrier, or “frequency-spanning” byoccupying a single symbol-time on multiple subcarriers (not to beconfused with time-division duplexing TDD and frequency-divisionduplexing FDD which pertain to duplexing of message pairs, and havenothing to do with the shape of each message in time-frequency space).“RF” or radio-frequency refers to electromagnetic waves in the MHz(megahertz) or GHz (gigahertz) frequency ranges. The “raw” signal is theas-received waveform before separation of the quadrature branch signals,and includes a raw-signal amplitude and a raw-signal phase. “Phasenoise” is random noise or time jitter that alters the overall phase of areceived signal, usually without significantly affecting the overallamplitude. “Phase-noise tolerance” or “phase-noise margin” is a measureof how much phase alteration can be imposed on a message element withoutcausing a demodulation fault. “Amplitude noise” includes any noise orinterference that primarily affects amplitudes of received signals.Interference due to competing signals is treated as noise herein, unlessotherwise specified. A “faulted” message has at least one incorrectlydemodulated message element. A “phase fault” is a message elementdemodulated as a state differing in phase from the intended modulationstate, whereas an “amplitude fault” is a message element demodulated asa state differing in amplitude from the intended modulation state. Theincoming signal to the receiver may be termed the “raw” waveform orsignal, which includes a “raw amplitude” and a “raw phase”. The receivercan then process the raw signal by separating it into two orthogonalbranches, as mentioned. The receiver can also combine the branchamplitudes to determine a “sum-signal”, which is the vector sum of the Iand Q branch signals and generally approximates the raw waveform. Avector sum is a sum of two vectors, which in this case represent theamplitudes and phases of the two orthogonal branches in I-Q space. Thesum-signal has a “sum-signal amplitude”, equal to the square root of thesum of the I and Q branch amplitudes squared (the “root-sum-square” of Iand Q), and a “sum-signal phase”, equal to the arctangent of the ratioof the I and Q signal amplitudes (plus an optional base phase, ignoredherein). Thus the sum-signal represents the raw received waveform of aparticular subcarrier, aside from signal processing errors in thereceiver—which are generally negligible and are ignored herein. As analternative to QAM, a message may be modulated in multiplexedamplitude-phase modulation, which generally provides better noisemargins than QAM. “Classical” amplitude-phase modulation (sometimes“polar” modulation, “star-QAM”, “APSK” amplitude-phase-shift keying,“PQAM” polar QAM, among many others) refers to message elementsmodulated in both amplitude and phase, wherein both the amplitude andphase carry message data. These jargon terms are avoided herein becausethey can change over time, and are ambiguous. As used herein,“amplitude-phase modulation” includes all modulation schemes in whichthe amplitude of a transmitted signal is modulated to carry message dataaccording to a set of predetermined amplitude levels, and the phase ismodulated to carry message data according to a set of predetermined,equally-spaced, phase levels. Hence PSK schemes are excluded since theyfail to encode message data in the amplitude, and QAM schemes areexcluded since they fail to encode message data in the phase. An“isotropic” or “non-directional” or “omnidirectional” beam hassubstantially uniform intensity around a horizontal 360 degrees.

As used herein, a “test signal” is a transmission with a predeterminedbeam configuration. The receiver can measure the signal quality receivedduring each test signal. Test signals may be configured as demodulationreferences or phase-tracking reference signals. Test signals may beconcatenated with other messages such as acknowledgements, grantrequests, uplink data messages, and so forth. A “feedback message” is ashort message transmitted in response to a test signal. The feedback maybe configured to select one particular test signal that provides thebest reception, from a plurality of test signals. Test signals areroughly analogous to the CSI-RS of prior art, but with greaterversatility and much lower resource consumption. Likewise feedbackmessages correspond to CSI but with many advantages detailed below. Thesignal quality may include the received amplitude or RSRP or SNR, aswell as other relevant parameters such as current noise andinterference. Uplink test signals correspond to SRS. In each case, thedisclosed versions include enhanced features and options, with benefitsvastly beyond the prior art, as described in the examples.

Turning now to the figures, the following examples show how test signalscan be provided with a downlink message, and a feedback message can beprovided with an acknowledgement.

Realtime Feedback

FIG. 1A is a schematic showing an exemplary embodiment of a downlinkmessage with beam test signals, according to some embodiments. Asdepicted in this non-limiting example, a unicast downlink transmission100 to a user device includes a message 102 and three test signals 103,104, 105. The message 102 may be an incoming data message from anothercell, or a control message from the base station, or other message. Themessage 102 may be a request for the user device to measure the threetest signals and reply which one provides the best signal quality. Themessage may include an identification code of the user device, or it maybe transmitted in resources that were previously granted to the userdevice for beam signal quality measurements. Alternatively, the message102 may be absent, and the transmission 100 may consist of just the testsignals 103-105, preferably at a pre-arranged time and frequency so theuser device knows that they are arriving. The transmission 100 may betime-spanning or frequency-spanning. Each of the three test signals mayoccupy a single resource element each, thereby adding three resourceelements to the length of the message 102. The test signals may bepositioned before the message, or some before and some after themessage, or all after the message as shown, or other distribution, solong as the user device knows where to find the test signals. Asmentioned, it would be helpful for the format and usage to be specifiedin wireless standards.

The three test signals may be transmitted with different transmissionparameters, such as different beam directions. The user device canmeasure the received signal quality of each test signal and thenindicate which test signal was received best, thereby enabling the basestation to direct its beams toward the user device. Alternatively, thetest signals may be transmitted with different beam widths, or differenttransmission power, or polarization, or encoding, or modulation, orpolarization, or frequency, or other feature that may be varied in awireless transmission. In each case, the user device may select which ofthe test signals was best received, and may indicate the favored testsignal in a feedback message to the base station. The user devicethereby effectively requests an increase or decrease in the transmissionparameter by choosing the test signal that is best received. The basestation then knows, according to the feedback message, which way theselected test signal was transmitted, and thus can apply an increase ordecrease in the transmission parameter.

In some embodiments, the user device can indicate, in the feedbackmessage, that two of the test signals were received with about the samehigh signal quality. By specifying the two best test signals, the userdevice can enable the base station to further refine the parameter beingvaried, for example by adjusting the transmission parameter to anaverage of the two best-received test signals.

The example is non-limiting, as mentioned. Other embodiments may includea different number of test signals, such as two or four or other number,according to the parameter variations being explored.

FIG. 1B is a schematic showing an exemplary embodiment of a downlinkmessage with beam test signals configured as demodulation references,according to some embodiments. As depicted in this non-limiting example,a transmission 110 is depicted including a message 112 with test signals113, 114, 115 and blank spaces 116, 117. The horizontal dimensionrepresents time or frequency, depending on whether the message 112 istransmitted time-spanning (in a single subcarrier) or frequency-spanning(in a single symbol-time). The blank spaces 116, 117 have notransmission, and thus demark the beginning and ending of thetransmission 110. Each test signal 113, 114, 115 occupies a singlereference element in this example, and includes two branches, such asthe I and Q branches of a QAM modulation scheme, or the amplitude andphase of an amplitude-phase modulation scheme, as suggested by thediagonal strike-through. Thus the blank spaces 116, 117 are shown withzero amplitude in both branches 111.

In this example, the first test signal 113 is a short-form single-pointdemodulation reference occupying a single resource element, in which theI-branch 118 is modulated according to the maximum predeterminedamplitude level of +3 arbitrary units, and a Q-branch 119 modulatedaccording to the minimum branch amplitude of the modulation scheme of−3. The receiver can then calculate all of the other predeterminedamplitude levels of the modulation scheme by interpolation. In theexample, the message 112 is modulated in the same modulation scheme, andtransmitted in the same way, as the first test signal 113. Hence thefirst test signal 113 provides a fresh calibration of the predeterminedamplitude levels at the start of the message 112. In addition, the lasttwo test signals, 114, 115 can serve as phase-tracking referencesignals. In addition, each demodulation reference 113, 114, 115 alsoprovides a measure of the current phase noise according to a ratio Q/Iof the received branch amplitudes, since phase noise typically rotatespower from one branch into the other branch. In addition, the blankspaces 116, 117 provide an indication of the interference level (bothamplitude and phase of the interference) at the two ends of the message112. In addition, the demodulation references (along with the blankspaces 116, 117) also provide a further indication of the starting andending points of the message 112, which may assist the user device infinding the message start and end.

In this example, the final two test signals 114, 115 are modulated inthe same way as the first test signal 113, but transmitted in differentbeam directions. For example, the first test signal 113 and the message112 may be transmitted in a particular direction θ, and the second andthird test signals 114, 115 may be transmitted in incrementallydifferent directions θ+dθ and θ−dθ, where dθ is the angular increment.The receiver can then indicate, in a feedback message, which beamdirection provides the best signal quality, such as the highestamplitude or SNR or received power, for example. As a further option,the final two test signals 114, 115 may be modulated differently fromthe first test signal. For example, the leading test signal 113 may have+3 amplitude units in the I branch and −3 in the Q branch, whereas thetrailing test signals 114, 115 may have −3 amplitude units in theI-branch and +3 units in the Q-branch. The difference in modulation maythereby indicate the start and end of the data message 112unambiguously, while still providing the same phase noise measurement atthe start and end of the message 112.

In a first embodiment, the message 112 may be a downlink messageincluding the test signals, from which the user device can providefeedback to the base station. The downlink message may be an incomingdata message from elsewhere. Alternatively, the downlink message may bea control message such as a grant or an RRC message or one of theinitial access messages such as the Msg2 or Msg4 of a 4-step initialaccess procedure, or MsgB of a 2-step initial access procedure, or otherdownlink transmission. In each case, the test signals 113, 114, 115 mayenable the user device to indicate, in a subsequent uplink feedbacktransmission, which of the test signals provided the best reception,thereby enabling the base station to aim its transmission beams moredirectly toward the user device.

In a second embodiment, the message 112 may be an uplink message. Theuplink message may be a data message from the user device, transmittedresponsive to a previous grant. Alternatively, the message 112 could bea scheduling request at a predetermined time, or a BSR messageresponsive to a BSR grant, or an acknowledgement, or one of the initialaccess messages such as the random access preamble, or the Msg3 of a4-step initial access procedure, or MsgA of a 2-step initial accessprocedure, or other uplink transmission. In each case, the test signals113, 114, 115 may enable the base station to indicate, in a subsequentdownlink feedback transmission, which of the uplink beam directionsprovides the best reception, thereby enabling the user device to aim itstransmission beams more directly toward the base station.

In a third embodiment, either entity (the user device or the basestation) may initiate a beam re-alignment procedure by transmitting abeam adjustment request to the other entity. The beam adjustment requestmay be transmitted on a contention-based channel such as the randomaccess channel or other channel allocated for grant-free messages, oranother channel allocated for user devices to request beam services. Thefirst entity's message may include a series of beam test signals such asthe (θ, θ+dθ, θ−dθ) sequence as discussed. The second entity can thenreply by transmitting a feedback message indicating the favored testsignal, thereby indicating the best beam direction. The feedback messagemay be transmitted on the same channel as the ping request. The feedbackmay also be multiplexed with a power adjustment request. In addition,the two entities can engage in mutual feedback, wherein the feedbackmessage includes test signals transmitted back to the first entity,which can then send a second feedback message to the second entityindicating best beam angle from its perspective, and optionally a secondpower adjustment for the second entity. In this way the user device andthe base station can both adjust their beam directions and power levelsrapidly, based on feedback from the other entity, using only minimalresources and negligible additional power consumption.

In a fourth embodiment, two user devices in an ad-hoc network maycommunicate in sidelink, and may adjust their beams and power levelsusing test signals and responsive feedback messages in a similar way,without participation of a base station.

In a fifth embodiment, two base stations or core networks maycommunicate wirelessly, and may maintain good beam contact by exchangingtest signals and responsive feedback messages, as disclosed.

By providing test signals with each downlink message, the base stationand the user device can cooperate to keep the downlink and uplink beamsaligned even as conditions (or positions) change, at low or negligiblecost.

FIG. 1C is a schematic showing an exemplary embodiment of a feedbackmessage, according to some embodiments. As depicted in this non-limitingexample, a user device transmits a feedback message that indicates whichtest signal has the highest received signal quality. Hence the feedbackmessage, indicating preference for a test signal with highertransmission parameter is equivalent to a request, by the user device,to increase the transmission parameter, and likewise for lower. Thefeedback message may further indicate whether the received signalquality of the best-received test signal is between a lower and an upperthreshold, or below both thresholds, or above both thresholds, and canthereby request to increase, decrease, or leave the transmission powerunchanged.

In the depicted case, the feedback message 120 includes two resourceelements 121, 122 encoding feedback information. In the first resourceelement 121 “RE-1”, a first modulation component, such as an I-branchamplitude, can indicate both an acknowledgement and a retransmissionrequest in various ways. In this example, the feedback message 120 ismodulated according to 9QAM, which is a modulation scheme that includes9 states symmetrically distributed around zero amplitude, with eachbranch modulated as +3, 0, −3 arbitrary amplitude units. Alternatively,the feedback message may be modulated in a 9-state amplitude-phasemodulation scheme with three phase levels multiplexed with threeamplitude levels. Both schemes include a zero-power state. Both schemesare well-suited for providing incremental feedback regarding twodifferent parameters, such as the beam direction and the transmittedpower, since the amplitude levels are well separated and readilyrecognized in a receiver. In addition, the 9 states can encode twofeedback parameters, including the +/0/−“one-of-three” incrementalselections as discussed, for both feedback parameters simultaneously,multiplexed into a single resource element.

In the depicted case, the I-branch 124 of the first resource element 121can indicate a successful reception (ACK) with an amplitude of +3arbitrary amplitude units, and an unsuccessful (“faulted”) reception(NACK) as 0 units (zero amplitude in this branch). The third state at −3amplitude may indicate an unsuccessful reception so that the transmitterknows there are problems, but nevertheless declining a retransmission.(The receiver may decline a retransmission for various reasons, such ashaving exhausted a number of allowed retransmissions, or being in atime-sensitive application where the retransmission would be too late,or other application in which a retransmission of a faulted messagewould not be useful.)

In the Q-branch 125 of the first resource element 121 of the feedbackmessage 120, the receiver can indicate whether an uplink grant isrequested (“SR”). In one embodiment, the +3 branch amplitude indicates“SR is requested, please send a BSR grant”, while the −3 branchamplitude indicates “SR is requested and the BSR is appended”, and the 0branch amplitude represents “SR is not requested at this time”. Thus +3amplitude indicates that a BSR grant is desired, −3 amplitude indicatesthat a BSR (buffer status report) is appended to the feedback messageand a message grant is desired, and zero amplitude indicates that nogrant is desired, in this example.

The second resource element 122 may include a power feedback indicator126 in the I-branch and a beam-angle selector 127 in the Q-branch, forexample. The power feedback indicator 126 may be +3 to request anincrementally higher transmission power, 0 to keep the power unchanged,and −3 to reduce the power by a predetermined increment. For example,the receiver can evaluate the amplitude (or SNR or SINR or RSRP or othermeasure of the received signal quality or reception reliability) of thereceived test signals. The receiver can then compare that signal qualityto a lower threshold and an upper threshold. If the signal quality isbelow the lower threshold, the receiver can request an incrementallyhigher transmission power, to meet reception requirements. If the signalquality is above the upper threshold, the receiver can request anincrementally lower transmission power, to save energy and avoidbackground generation. If the signal quality is between the lower andupper thresholds, the receiver can indicate “keep same” using thezero-amplitude feedback value. The upper and lower thresholds, and theincrement size, and other parameters, may be determined by the networkand may be indicated in system information files upon initial access,and may be revised when necessary using a broadcast message or an RRC.Alternatively, the user device can set its own upper and lower signalquality thresholds based on the requested QoS, according to networkguidelines. As a further option, the user device can set the upper andlower thresholds based on the current fault rate or the like, asexperienced by the user device.

The other branch of the second resource element 122 may provide beamalignment feedback. For example, the branch amplitude may indicate whichtest signal, of three received test signals, provides the bestreception. The feedback may then allow the base station to adjust itsdownlink beam 127 direction toward the user device. Alternatively, thebase station may vary another transmission parameter such as the angularwidth of the beam or some other parameter. For example, the first testsignal and the data message may be transmitted in a previously selecteddirection θ, and the final two test signals 104, 105 may be transmittedin two incrementally different directions ±dθ. For example, the threedirections may be termed θ−dθ, θ, and θ+dθ in which dθ is apredetermined angular increment. The receiver can then measure thereceived signals quality of each test signal, and may indicate, in thebeam feedback 127, which one was received best. For example, the beamfeedback amplitude may be +3 units if the θ+dθ direction is best, zeroamplitude if the original message beam direction θ is best, and −3 ifthe θ−dθ direction is best. In this way, the transmitter can update thebeam direction after each downlink message, thereby adjusting the beamdirection as needed, and can thereby mitigate changing propagationconditions (or motion of the receiver, or diffraction, or changingobstructions, rain, etc.) in realtime upon each message.

The network can determine the size of the increment based on the user'sprevious feedback choices. For example, the base station may initiallyuse a large increment, such as the full angular width of the transmittedbeam, to rapidly converge on the general direction toward the userdevice. The base station may then switch to a much smaller increment(such as 1/10 of the beam width) for optimizing or fine-tuning thedirection. In another embodiment, the transmitter can switch among abinary sequence of increments such as 2, 1, ½, ¼, ⅛ of the beam width.

The base station can change the increment size depending on the userdevice's previous choices. For example, if the user device requests anincrease in the beam angle twice in succession, the base station canincrease the size of the increment, for faster convergence on the bestdirection. Likewise if the user requests a decrease in angle repeatedly,the increment size can be increased in the same way. Increasing the sizeof the increment upon repeated like-sign requests may be termed“escalation”. In addition, the base station can reduce the incrementsize when the user device requests either no change, or a change in theopposite direction, (size reduction termed “de-escalation”). By varyingthe increment size in this way, the base station can converge rapidly onthe optimal beam direction toward the user device.

The transmitted power can be incrementally adjusted in a similar way,based on the user device's feedback. The base station may use variousincrement sizes for power adjustment, based on the user device'sprevious feedback requests. For example, the user device may requirethat the received signal quality be within a lower and an upper signalquality thresholds, and may request higher or lower transmission powerrepeatedly until the signal quality is within those thresholds.

An advantage of such incremental feedback may be that the user devicegenerally does not know, and does not need to know, the variousincrement sizes, nor the actual beam power levels, nor the particularbeam angles involved, nor even which transmission parameter is variedamong the test signals. The responsibility of the user device is toselect which test signal is best received, and to indicate thatselection in the feedback message 120. In addition, the user device mayprovide feedback regarding the signal quality in that best-received testsignal, so that the base station can adjust its transmission power, asdiscussed above. Another advantage may be that the user device canprovide incremental feedback on two parameters, such as beam angle andbeam power, in a single feedback resource element. Another advantage maybe that the depicted modulation scheme does not require amplitudecalibration for unambiguous reception, so long as the receiver issynchronized with the base station, according to some embodiments.

In addition, if the user device has a beamforming capability, the basestation can assist the user device in aligning the user device's uplinkbeam. For example, the base station may inform the user device of theoptimal downlink beam direction when it has been determined, so that theuser device can then aim its reception beam in the 180-degree oppositedirection. Alternatively, or in addition, the base station can transmitmultiple identical test signals which the user device can receive whilevarying the reception beam direction. By either method, the user devicecan determine which reception beam direction provides the bestreception, and can thereby cause its transmission and reception beams toaim toward the base station.

A particular case may occur, in which the reception has failed (NACK),and no SR is requested, and no change in power or beam angle isrequested. In that case, the feedback message would be zero, that is,both branches of both resource elements would have zero amplitude. Thefeedback message would include no transmission at all. The base stationmay interpret the absence of an acknowledgement as indicating that theuser device did not receive the message, or it may interpret the nullfeedback as a feedback message including NACK and requesting no changes,as described, or it may assume that the acknowledgement has been somehowmisdirected, or other mishap. In all such cases, however, the responseis the same; retransmit the message. Hence in this case the 0,0 state ismeaningful despite having no transmitted energy, and has the intendedeffect which is to prompt a retransmission. Notably, in this case, thecorrect feedback (a retransmission request with no changes) is conveyedto the base station, at zero cost in transmitted power.

FIG. 1D is a schematic showing another exemplary embodiment of afeedback message, according to some embodiments. As depicted in thisnon-limiting example, a feedback message 140 includes four resourceelements 141, 142, 143, and 144. The first resource element 141 is anidentification code that indicates, to the base station, which previousdata message is associated with the feedback message 140. Often the basestation transmits several data messages in the same downlink interval,so the base station embeds a code in each data message foridentification. The user device then duplicates that code in the firstresource element 141 of the feedback message 140, thereby indicatingwhich data message is referred to in the feedback message. In othercases, the base station may grant specific resources for the feedbackmessage 140, in which case the identification code 141 may not benecessary.

The second resource element 142 includes an acknowledgement multiplexedwith a scheduling request, such as 121 of the previous figure, and thethird resource element 143 includes a power adjustment and a beamselector, such as 122 of the previous figure.

The fourth resource element 144 is a BSR message. The BSR 144 indicatesthe size of a planned subsequent uplink message. The BSR may be includedor appended to the feedback message when the user device requests amessage grant. For example, the user device can request a message grant,and can indicate that the BSR is present, in the second resource element142 of this example, or 122 of the previous example. The base stationthen interprets the BSR as indicating the size of the requestedresources to accommodate the planned message. If, on the other hand, theBSR message is not included, then the base station can interpret the SRrequest as requesting a BSR grant, thereby requesting resources foruploading the BSR only.

It may be noted that the modulation scheme employed for theidentification 141 and the BSR 144 is quite different from themodulation scheme used for the two feedback elements 142-143, that is,16QAM versus 9QAM in the depicted case. The receiver is expected to knowhow to demodulate each resource element.

In this example, the receiver has identified which data message isassociated with the feedback message, and has acknowledged the datamessage as faulted or unfaulted, and has requested a retransmission ifneeded, and has requested an uplink grant if needed, and has requested apower adjustment if needed, and has selected a beam angle adjustment ifneeded, and has specified the size of the requested uplink grant ifneeded, all at a cost of just four resource elements.

FIG. 1E is a schematic showing an exemplary embodiment of a resourcegrid with test signals, according to some embodiments. As depicted inthis non-limiting example, a resource grid 160 including two slots intime and two resource blocks in frequency, includes resource elementswith a specific symbol-time and subcarrier frequency. Afrequency-spanning message 161 includes a leading test signal 162 andtwo trailing test signals 163, all occupying a number of subcarriers ata single symbol-time. Also shown is a time-spanning message 164 withleading 165 and trailing 166 test signals. Dashed lines demark controlregions 167. The rest of the area is allocated for downlink orunscheduled, thereby accommodating the two messages. Due to the controlzone 167, the time-spanning message 164 breaks and then continues afterthe control zone 167.

As mentioned, the examples are non-limiting. The example messages mayinclude other resource elements than those depicted, and one or more ofthe depicted resource elements may be altered or removed, and they maybe in another order, and the branches (or other components) of theresource elements may be modulated or encoded in various ways other thanthose depicted, and may represent different requests or choices thanthose described, without departing from the disclosed principles.

FIG. 2 is a flowchart showing an exemplary embodiment of a method forproviding feedback to a base station, according to some embodiments. Asdepicted in this non-limiting example, actions of a base station areshown on the left and of a user device on the right. At 201, the basestation transmits a data message surrounded by three test signals(“sig-1” etc.), represented here by a first, second, and thirddemodulation reference, each at an incrementally different beamdirection (at θ and θ±dθ) but with equivalent modulation and the sametransmission power. Optionally, at 202, the test signals can beshort-form type demodulation references that occupy a single resourceelement while exhibiting sufficient modulation levels so that thereceiver can readily calculate the remaining modulation levels byinterpolation. Optionally, at 203, each of the test signals may beconfigured with orthogonal I and Q branches, each transmitted with themaximum or minimum branch amplitude level, so that the receiver candetermine phase noise according to the ratio of the branch amplitudesas-received. Optionally, at 204, the data message, with the test signalsattached, may be preceded and/or followed by a blank resource elementwith no transmission therein, so that the receiver can evaluate noiseand interference both fore and aft of the transmission, and also todemark the boundaries of the transmission.

At 205, the user device receives the first test signal, the datamessage, and the second and third test signals, plus blank resourceelements. At 207, the user device demodulates each message element ofthe data message using the first test signal as a demodulation referencefor amplitude calibration, and the second and third test signals as aphase calibration.

At 208, the user device determines whether the data message is faulted,for example by comparing the data message (or a digest or hash thereof)to an error-detection code such as a CRC or parity construct, associatedwith the data message.

At 209, the user device compares the amplitude or power or signalquality in the three test signals, and determines which one has the bestreception, thereby indicating which beam direction produced the bestreception.

At 210 (if not sooner), the user device determines a SNR or othermeasure of signal quality of the best-received test signal, and therebydetermines whether additional transmitter power is needed. For example,the user device can compare the signal quality of the best test signalto two thresholds—a higher threshold and a lower threshold. If thesignal quality is below the lower threshold, additional power is needed.If it is above the higher threshold, excess power is being wasted. If itis between the two thresholds, no power change is needed.

At 211, the user device transmits a feedback message to the basestation, including a multiplexed acknowledgement, a retransmissionrequest if needed, a scheduling request if needed, a power adjustmentrequest if needed, and the beam selection based on the three testsignals.

Optionally, at 212, the user device can add an identification code tothe feedback message. The identification code may be extracted from thedata message. The identification code may enable the base station todetermine which data message is referred to. However, if the basestation has already allocated specific resource elements for thefeedback message, the base station would already know which data messageis involved, and no identification code is needed. Optionally, at 213,the user device can append a BSR message for the requested uplink grant.

At 214, the base station receives the feedback message and adjusts thetransmitted power level of future downlink messages. The base stationalso adjusts the beam direction toward that user device. The basestation can then provide a retransmission of the data message ifrequested, and a grant for a subsequent uplink transmission ifrequested.

In some embodiments, the base station can vary the increment sizedepending on the user device's pattern of requests. In addition, thebase station can apply the requested incremental change in the beamangle first, using the current increment size, and then increase ordecrease the increment size depending on the user device's priorfeedback messages. In other embodiments, the base station can escalatethe increment size first, and then apply the adjustment to the beamangle or power using the updated increment size. The first way (rotatebefore escalating) is more conservative and stable, but the second way(escalate first, then rotate) may converge faster. The base station canselect whether to apply the current or updated increment size whenadjusting the power or beam angle. There are many other operationalvariations involving incremental feedback, which the base station orcore network can determine.

In some embodiments, the base station or core network can record ahistory of past experience with incrementation, including externalconditions, incrementation parameter choices, and user actions. The basestation can use the history data in determining how to perform theincremental adjustments or set the incrementation parameters. Forexample, the base station can use machine learning to discern trends andpatterns in how the incrementation choices subsequently affect networkoperations.

Thus the user device, in cooperation with the base station, has arrangedto receive and demodulate the data message, and has requested aretransmission if needed, and has enabled the power and beam directionto be incrementally adjusted, and has obtained an uplink grant, with theminimal expense of just two to four resource elements in the feedbackmessage, and a single extra resource element with the data message forthe third demodulation reference.

AI Model for Feedback

Many feedback procedures are well-suited for automatic management by anartificial intelligence model. AI excels at complex, multidimensionaldecision-making. Often, an AI model can correct for subtle problems thateven an expert human cannot discern. An AI model may be tuned usingmachine learning with large amounts of network data. After tuning, theAI model may assist both network managers and user devices, ingenerating and responding to feedback messages. The examples belowillustrate AI models for determining the signal quality of test signals,for incrementally adjusting transmission beam parameters, and forselecting a modulation scheme to minimize faults and maximizethroughput. Each specialized AI model is discussed in more detail below.

FIG. 3 is a schematic showing an exemplary embodiment of an AI structurebased on a neural net, according to some embodiments. As depicted inthis non-limiting example, inputs and outputs are arranged to form an AImodel configured and trained to assist a base station by predictingnetwork performance under various conditions such as setting aparticular transmission power level. Other embodiments may be trained todetermine signal quality, manage incrementation, or select anadvantageous modulation scheme.

A predictive AI structure 360 may be configured as a neural net, or asanother type of artificial intelligence or machine learning structurethat provides a choice or a prediction, based on input factors such asuser requirements and limitations, network parameters, and current faultrates, among others.

The depicted AI structure 360 includes an input layer 361 of inputparameters represented as boxes, one or more output 367 value(s)represented as a triangle, and two layers of internal functions 363 and365 represented as circles. (The various items are sometimes called“nodes”, not to be confused with the nodes of a wireless network.)Weighted directional links 362 indicate the flow of input data from theinputs 361 to the first internal layer 363, and additional weighteddirectional links 364 indicate the flow of processed data from the firstinternal layer 363 to the second internal layer 365, and furtherweighted directional links 366 indicate the flow of processed data fromthe second internal layer 365 to the output 367.

When provided with specific input parameters, and “trained” or adjustedto solve a particular problem based on real operational data, the AIstructure 360 becomes an “AI model” that predicts network performancefollowing a transmission power level adjustment, in the depicted case.The prediction is according to the input parameters, and therebydetermines the effects of setting the transmit power level of messagesin various ways.

The inputs 361 may include network operational parameters 368 such asthe current message failure rate, the current throughput (in messagesper second or bits per second, for example), the frequency of messagesthat do or do not obtain their desired QoS or QoE, the average messagedelay or latency, the number of active user devices in the network, thegeographical extent of the network, the presence of mobile or fixed userdevices, among other network parameters. The inputs 361 may furtherinclude parameters of the current message 369, such as the transmitterpower used in transmitting the current message, the distance between thetransmitter and receiver, the size of the message, the QoS or QoEexpected by the recipient for the current message, whether the recipientis currently obscured and by how much, whether a prearranged fee orother financial arrangement is in place (with either the recipient orthe message originator), whether the message is one of a series ofmessages or fragments, and other message parameters. The inputs 361 mayfurther include external or environmental factors 370 such as thecurrent noise or interference level from external sources, whether thebase station has recently received complaints of interference from otheradjoining networks, the spatial density of active user devices in thecurrent cell and in the geographical area, among other external factors.Further inputs may pertain to various modulation schemes 371 or measureddata regarding signal quality 372.

In addition, a final input 374 is the measured network performance,acquired after the message has been transmitted and the requestedchanges applied. However, this input 374 is not provided to thestructure; it is used as a training value. In some embodiments, thenetwork performance 374 may be quantified as a single metric 375, suchas the throughput minus twice the message failure rate, or as aplurality of calculated values, such as the throughput, the retransmitrate, and the average delay per message, for example. The predictedoutput 367 may be cast in the same form. Then the measured networkperformance metric 375 can be compared with the predicted output 367 toevaluate the accuracy of the AI model predictions.

The weighted directional links 362, 364, 366 may include mathematicaloperations, such as multiplying the output from each node of a previouslayer by a predetermined coefficient, and then passing the product toone or all of the nodes of the next layer, among other possiblecomputation. In some embodiments, the links 362 and 364 may perform nocomputation, and all functionality is contained in the internalfunctions 363 and 365. Although links are shown in the figure connectingeach node to just a portion of the next layer for clarity, in someembodiments every node of each layer is linked to every node of the nextlayer.

The internal functions 363 and 365 may include any mathematical orlogical functions of the inputs. In various embodiments, each internalfunction 363 or 365 may include arithmetic or mathematical formulas,nonlinear functions (such as exponential or arctangent compressionfunctions), Boolean logic (such as, “take input A if input B is largerthan C, and take input D otherwise”), among many other functionaloptions. Each internal function 363 and 365 may include one or morevariables or adjustable parameters. In some embodiments, the internalfunctions 363 and 365 perform the same operations on all of theirinputs, whereas in other embodiments the internal functions process eachdata flow from each connected link differently. In some embodiments, thedirectional links are simply passive conduits, and all the weightingfactors and calculations are included in the internal functions. In someembodiments, the structure 360 may include feedback or bidirectionallinks or other complex topology not depicted here.

The output 367 is, in this case, a prediction of the subsequent networkperformance after adjusting the transmission power level. The networkperformance output 367 may be quantified as a performance metric, asmentioned. The output 367 may thereby indicate how the choice oftransmission power influences the subsequent performance of the network.For example, if the message is sent with too much power, interferencecomplaints from adjacent cells may increase, whereas if the message issent with insufficient power, the message failure rate may increase anddelays (such as retransmission delays) may increase. In someembodiments, network examples with similar parameters may be clusteredor averaged, and the averaged parameters may be presented to the AImodel as inputs, thereby saving computer time and potentially exposingmore subtle effects.

The variables and weighting factors and other adjustable variables maybe adjusted to “tune” or “train” the model based on network data. The AImodel may initially start with the adjustable variables in arbitrarystates, or set by logic or intuition, or otherwise. Then, data fromactual network activity may be used as the inputs 361 and the AI modelmay calculate (or predict) a subsequent performance metric 367.Specifically, the data may include “pre-transmission” data, which isdata measured before the message is transmitted. The prediction is thencompared 363 to the measured “post-transmission” performance metric 362of the network, measured subsequent to the message. In training the AImodel, each variable (or a group of variables) may be adjusted in some“direction”, and the prediction may be again calculated and comparedwith the actual metric. If the prediction is improved by that variation,the variables may be adjusted further in the same direction; but if theprediction is worse, the variables may be adjusted in the oppositedirection or in some other direction. In each adjustment or series ofadjustments of the variables, the most influential variables may bedetermined empirically, either by varying them individually or bytracing backwards from the output 367, and subsequent variations mayfocus primarily on those influential variables. This iterative trainingprocess, of repeated variation and comparison of the prediction, may berepeated for a large number of different message types with differenttransmission power levels under different network situations until,eventually, a particular set of values may be found that providessatisfactory predictions of network performance across a wide variety ofsituations and transmission power levels.

The next step may be to prepare an algorithm that base stations can useto determine how much transmission power to employ in transmitting eachparticular message. In some embodiments, the algorithm may be the AIstructure itself, but with the variables frozen at the most advantageoussettings. For ease of use by the base stations, the algorithm may beconfigured to specify the preferred transmission power level instead ofpredicting the network performance metric. In addition, the algorithmmay be simplified by removing the least-productive inputs and internalfunctions and links. In other embodiments, the algorithm may be distinctfrom the AI model but based on it. For example, the algorithm may be acomputer program or subroutine, an interpolatable tabulation of values,or a graphical device, among many other calculation means for specifyinga particular transmission power level for each message according tomessage parameters, current network conditions, and observedenvironmental inputs. The base station can use the algorithm to selectan appropriate power level and then transmit the message with theindicated power level.

In another embodiment, the base station or user device operating an AImodel may adjust certain variables in the AI model based on the currentnetwork experience. The entity developing the AI model originally (suchas a supercomputer) may provide, to the base station or user device, anindication of which variables can be adjusted in response to certainevents. Such a set of variables that can be altered to produce aspecific effect may be termed a “solution vector” and passed to the enduser for guided adaptation of the model. For example, the modeldeveloper may determine that, in some hypothetical situation, thenetwork performance could be enhanced by adjusting the model variablesaccording to a particular solution vector, such as placing higherimportance on avoiding message faults and less importance on pushingthroughput, to consider one hypothetical example. The developing entity,such as the supercomputer, may indicate such advice to the base stationor user device that employs the AI model, so that the end user can adaptthe model to its own situation empirically. By adapting the AI modelaccording to the network or user experience, the predictions and otheroutputs may be optimized for each operating entity and each particularcommunication environment, thereby further enhancing operations andmultiplying the value of the AI model.

When AI-derived algorithms are used by base stations and/or user devicesto select appropriate transmission power levels of messages, orbeneficial modulation schemes, or optimal beam angles, or realtimeadjusted incrementation parameters, the network may achieve improvedreliability, lower latency due to fewer delays, higher energy efficiencyby avoiding wasted power, and improved overall network performance,according to some embodiments.

In some embodiments, the receiver may employ an AI model, or analgorithm derived from an AI model, to assist in determining the signalquality. The signal quality as used herein may be a composite parameterthat accounts for the received amplitude or power, interference andnoise, changing propagation conditions, accumulated delays ordemodulation faults, and a host of other real-world effects that caninfluence the determination of which test signal provides the best“signal quality” in the current situations. For example, if a userdevice is measuring the amplitude or received power of test signalswhile an adjacent cell is intruding with transmissions at a similarfrequency, the interference may distort the received signal, biasing theselection of the best-received test signal. Such interference may defeatthe prior-art channel-state procedures that are based on received poweralone because the interference power simply adds to the message receivedpower, defeating the purpose of the prior-art feedback. An AI model, onthe other hand, may correctly account for the intrusion by accountingfor the SNR of the test signal, the power received during a blank (notransmission) resource element, and other influences. Thus the AI modelmay correctly evaluate signal quality in many situations where theprior-art procedures may fail. With reliable determinations of thereceived signal quality of the test signals, the user device can thenprovide useful feedback requests to the base station, according to someembodiments.

AI-Managed Signal Quality Determination

The following example shows how to use an AI model for determining whichtest signal provides the best reception.

FIG. 4A is a flowchart showing an exemplary embodiment of a method forevaluating signal quality using an AI model, according to someembodiments. As depicted in this non-limiting example, an AI modelevaluates the signal quality based on user data. At 405, the user deviceprovides, to the AI model 406, a plurality of input values related tothe signal quality. For example the inputs can be data related to thetest signals, such as the received amplitude or power level of each ofthe test signal, the SNR or SINR observed with each test symbol based onthe raw waveform, deviations in amplitude or phase relative to thepredetermined amplitude or phase levels of the modulation scheme, andthe amplitude or phase variation during the test signal. The inputs canalso be related to current network operations such as an amount ofinterference coming in from outside the cell (measured in a blank,no-transmission resource element), as well as fault rates or bit-errorrates detected.

At 406, the AI model performs the analysis it has been tuned for, bydetermining a signal quality value at 407 for each test signal. In thisexample, the AI model, or a second AI model, or algorithm, determines anuncertainty in the signal quality which was calculated or estimated bythe AI model. The uncertainty in the estimated signal quality value isimportant because the estimate would be worthless if the uncertainty istoo large. The user device can compare the signal quality values of thetest signals, as determined by the first AI model, with theuncertainties as determined by the second AI model (or an extension ofthe first one, or an algorithm), and can then decide whether to use thesignal quality values of the test signals for feedback. If theuncertainty is high, the recommendations of the AI model should bedisregarded, and the feedback opportunity should be skipped. Inaddition, when a feedback message is required yet the uncertainty is toohigh for reliable conclusions, the user device can configure thefeedback message for “no change”, and thus retain the possibility ofmaking a better determination at a later time.

FIG. 4B is a flowchart showing an exemplary embodiment of a method fordetermining the signal quality of the received test signals, accordingto some embodiments. As depicted in this non-limiting example, a userdevice uses an AI model to evaluate the signal quality of three testsignals, using inputs and outputs as disclosed in the previous figure.The user device then transmits the selected choice back to the basestation in a feedback message. Optionally, the user device may thenmonitor performance metrics and adjust the AI model variablesaccordingly, thereby continually improving the AI model.

At 411, the user device receives or develops or determines or otherwisearranges to use an AI model. In this example, the variables of the modelhave already been adjusted for evaluating a signal quality according toinput parameters, for example using a supercomputer with machinelearning on a large network data set. In other embodiments, the userdevice may adjust the model variables empirically, for example based onthe user's experience. As a further alternative, the user device canreceive periodic updates to the variables, and/or other updates to theAI model, based for example on the accumulated experience of many userdevices.

At 412, the user device receives a message from the base stationincluding several (such as 3 or 4) test signals. Each test signal istransmitted in a different way, such as at a different angle. The userdevice does not know, or care, how the test signals were transmitted inthis case; the user device is only responsible for determining whichtest signal is best received.

At 413, the user device evaluates various parameters of the test signalssuch as the received amplitudes or power levels, the SNR/SINR of eachtest signal, the stability or variations in amplitude or power or phaseduring each test signal, and the like. The user device can also comparethe amplitude and/or phase of each test signal to a predeterminedamplitude or phase level, and can thereby determine a deviation betweenthe received amplitude or phase and the closest predetermined level, foreach branch of each test signal. The user device can also determine theinterference level according to signals received during a blank (notransmission) resource element proximate to the test signals. Highinterference generally increases the uncertainty of the signal qualitydetermination, even if the RSRP is high.

In some embodiments, each test signal may be configured as ademodulation reference, which the user device can use for demodulatingthe message. The demodulation reference may be a short-form demodulationreference that exhibits the maximum amplitude or phase levels of themodulation scheme, or the maximum and minimum amplitude levels, or otherpredetermined modulation levels in a single resource element or at mosttwo resource elements.

At 414, the user device can provide the various parameter measurementsof the test signals as inputs to the AI model. At 415, the AI model cananalyze the inputs and determine the outputs, which at 416 may includethe signal quality of each test signal. Optionally, the AI model canalso provide outputs representing an uncertainty in each signal qualitydetermination. Alternatively, another AI model, or another algorithm,can evaluate the uncertainty. The evaluation of the uncertainty may usethe same inputs as the AI model that determines the signal quality, orthey may be different inputs. If the uncertainty is large, such ascomparable to the signal quality value or more, the user device maychoose to ignore the signal quality value, and thereby avoid causingerratic changes based on a poor data.

It may be noted that the uncertainty in the signal quality determinationis not a surrogate for the signal quality itself. If the signal qualityis low, the transmission power needs to be increased, even if theuncertainty is larger than the low signal. Usually, higher transmissionpower increases the signal quality and decreases the uncertainty,leading to better evaluations of the uncertainty thereafter.

At 417, the user device can determine whether the uncertainty, in thesignal quality determination of each test signal (and especially thebest-received test signal) is above or below a predetermined threshold.If the uncertainty is above the threshold (that is, if the uncertaintyis too high), the user device may ignore the results. For example, ifthe threshold equals 50% of the signal quality value or more, the userdevice may discard the results and provide no feedback, or the user cantransmit a feedback message requesting no changes, notwithstanding theprevious paragraph.

On the other hand, if the uncertainty is below the threshold, then at418 the user device can transmit a feedback message to the base stationindicating which test signal is favored. Optionally, the feedbackmessage may include other feedback items such as an indication ofwhether more or less transmission power is needed, based on the signalquality of the best-received test signal. The base station can thenimplement that choice by adjusting the transmission beam angle, andoptionally the transmission power, or other parameters.

Optionally (in dash) the user device can seek to update its AI modelvariables based on further performance data acquired by the user device.The user device may thereby customize the AI model according to the userdevice's experience, capabilities, wireless environment, etc. Forexample, at 419, the user device can continue to monitor the performancemetrics after transmitting a feedback message to the base station, andthereby determine whether the requested change succeeded in improvingthe performance, or the reverse. The performance metrics of interest tothe user device may include its success rate, throughput, fault rate,average delay, received SNR, or other measurables related tocommunication.

At 420, the user device can determine whether the performance is betteror worse than it was before the change in beam properties. If better,the user device can reinforce the AI variables at 422, for example bycausing them to be more resistant to future variations. However, if theuser device experiences worse communications after the requested beamchanges, this may indicate that the user device's AI model, tasked withevaluating the signal qualities of the test signals, was incorrect. Thatis, the AI model incorrectly evaluated the signal qualities of the testsignals, and in fact one of the other test signals was better than theselected one. In that case, at 421 the user device can adjust one ormore variables in the AI model in an attempt to improve the accuracy ofthe model, or at least to avoid the current disappointment.

In one embodiment, the user device can change the AI model variables atrandom, and can attempt to determine whether the resulting performanceis better. This scattershot approach generally takes a long time forconvergence, even when done by a supercomputer. More preferably, inanother embodiment, the user device has foreknowledge of which variablesto adjust under particular circumstances or to obtain particularresults. For example, the entity (such as a supercomputer) thatdeveloped the AI model may have determined which variables should bealtered, and in what direction, to mitigate various types of problems orto have some desired effect. As mentioned above, the set of alterationsin the model variables may be termed a “solution vector” when it isrelated to an intended change in performance. The AI developmentsupercomputer, or another entity, may vary the model variables invarious combinations, and may analyze the effect of each such variation,and in this way may have determined which variables can be adjusted tocounter certain problems. With knowledge of the solution vector and theperformance that it is intended to improve, the user device can thenselect which variables to adjust, and in which direction, according tothe problems observed. Preferably any changes made to the modelvariables, by the user device, are small incremental changes, which canbe reversed if desired. In addition, the user device may record ahistory of changes made to the AI model variables, so that the changescan be reversed if the predictive power of the AI model deterioratesrather than improves.

As a further beneficial option, at 423 the user device can transmit theresults of its experience with the model to other user devices, or to acentral AI model developer that accumulates experiences from many usersover time, thereby gradually improving the predictive power and utilityof the model. In addition, at 424 the user device can receive updatedvalues of the AI model variables, or a whole new AI structure, from theAI manager.

Incremental Feedback

The following examples show how a base station can adjust transmissionparameters incrementally, responsive to a user request in a feedbackmessage. For incremental adjustment of a transmission parameter, thebase station can apply a predetermined increment value to the parameter.The sign of the adjustment is determined by the user device's request inthe feedback message. This process continues until the user device findsthat the current parameter setting is better than any of thealternatives offered in test signals, and therefore requests that theparameter remain at the current available value.

The increment size (or step size) may be variable and controlled by thebase station. For example, the base station may vary the increment sizeaccording to the user device's previous incrementation requests. Forexample, if the user device repeatedly selects the test signal thatcorresponds to a positive increase in the beam angle, the base stationcan begin using larger increment sizes, and therefore may reach theoptimal angle more quickly. However, when the user device finallyreverses direction and begins requesting the opposite sign ofadjustment, the base station can revert to a smaller increment size forfine-tuning the angle.

To consider a specific case, regarding the beam angle adjustment, theincrement size can be set initially to 2 degrees say. If the user deviceselects the positive increment twice in succession, the base station candouble the increment size to 4 degrees. As mentioned, this increase inthe increment size is called “escalation”. If the user device thenselects a beam direction opposite to the previous change, the basestation may switch to a smaller increment such as 1 degree, whichthereby enables fine-tuning of the angle. Thus the base station can useincreasingly larger increments as long as the user device requestsincrements in the same direction, and then can revert to smallerincrements when the user device reverses and requests the oppositedirection, or requests no change. The base station can thereby achievethe optimal angle more rapidly than otherwise. In addition, the sameescalation and de-escalation processes can be applied to the incrementalpower level adjustments and other parameter adjustments served by thefeedback message, according to some embodiments.

FIG. 5A is a schematic showing an exemplary embodiment of threebeam-alignment test signals, according to some embodiments. As depictedin this non-limiting example, a base station 521 transmits threedirected beams 522, 523, 524 in various directions, and the amplitudesreceived by the user device 525 are shown schematically. A first testsignal 533 and the message 528 are transmitted on the middle beam 523.Two additional test signals 532, 534 are transmitted on the other twobeams (dashed) 522, 524, which deviate in positive and negative waysfrom the central beam.

The message 528 and the first test signal 533 are transmitted on thecentral beam 523, shown in solidline, because it was previouslydetermined to provide adequate reception at the user device 525.However, in this case, one of the other beams 524 is now better alignedwith the user device 525. After receiving and measuring the three testsignals, the user device 525 has determined that the original beam 523is not the best one, because one of the other test signals (534,transmitted on beam 524) is better received. The user device can thentransmit a feedback message to the base station 521 indicating which ofthe test signals was received with the best signal quality, which inthis case would be the last one 524.

A blank resource element 526 is received before the first test signal533, and a second blank resource element 531 follows the last testsignal 524. A dashed line is shown separating the first test signal fromthe message 528. Other two test signals 532, 534, are after the message.In this case, each test signal 532, 533, 534 is also a demodulationreference. The first test signal 533 is transmitted in a particulardirection indicated by θ, and the message 528 is also transmitted in thesame direction θ. The last two test signals 532, 534 are transmitted inother angles θ+ and θ− which are transmitted at higher and lower anglesrelative to the central beam 523. The height of the signal during thethird test signal 534 indicates that the best reception is obtained withthat beam 524.

The user device can then transmit a feedback message to the base station521 indicating that the best signal was obtained with the last testsignal 534. The base station 521 can then determine that the favoredtest signal 534 corresponds to the beam 524 with a lower angle, andtherefore may incrementally adjust its transmission beam angle to asmaller angle.

The user device 525 does not need to know which test signal istransmitted in which direction, or the size of the increment betweenadjacent beam directions, or even that beam alignment is being tested.It is sufficient, in this example, for the user device 525 to determinewhich test signal was best received, and to indicate that selection in afeedback message. The base station 521 is expected to know what to dowith the feedback information, such as to incrementally adjust the beamdirection toward the user device, whereas the user device 525 has nosuch responsibility. In contrast, some prior-art CSI procedures requirethe user device to figure out what the base station is supposed to doregarding the transmission channel, and then tell the base station howto do it, which represents a substantial burden for the user device.Therefore, in the depicted version, responsibility for interpreting thefeedback in terms of the transmission beam parameters is allocated tothe base station, thereby allowing the user device to focus on its owntasks, while leaving network management to the base station.

In a first embodiment, the angular difference between the central beam523 and each of the other two beams 522, 524 equals the currentincrement size. The base station will adjust the transmission beam toequal whichever test signal is favored by the user device, after whichthe base station may or may not change the increment size, depending onescalation rules. The user device can therefore determine the signalquality expected at the new beam angle already, since the user devicehas determined the signal quality in the favored test signal.

In a second embodiment, the angular difference between the central beam523 and each of the other two beams 522, 524 may be arbitrary and notrelated to the increment size. In that case, the test signals merelyindicate a direction of change of the beam angle that the user desires.The base station then applies the predetermined increment, in theselected direction. For example, the base station may first adjust theincrement size according to certain rules, and then apply that incrementin the requested direction. Alternatively, the base station may applythe current increment value in the requested direction, and then updatethe increment size. In each case, the subsequent messages to the userdevice are then transmitted at the newly updated beam angle.

As mentioned, a user device with beamforming capability can transmittest signals, and receive feedback responses, from a base station, andthereby keep the uplink transmission beam parameters adjusted properly.Likewise, two user devices communicating with each other, independent ofa base station, can provide the same kind of feedback at negligiblecost.

FIG. 5B is a schematic showing an exemplary embodiment of iterativeadjustments of beam angle, according to some embodiments. As depicted inthis non-limiting example, the downlink transmission beam angle isvaried incrementally, in response to the user device's signal qualityfeedback. Each downlink message contains three test signals. The userdevice indicates, in a feedback message, which test signal provided thebest signal quality. The three test signals may be transmitted in threedirections. For example, one of the test signals can be transmitted in apreviously established “baseline” beam direction, and the other two testsignals may be transmitted plus or minus a predetermined incrementangle, relative to the baseline direction. Thus the base station caniterate toward the ideal beam direction in a series of directedincrements.

In this example, the base station varies the size of the incrementaccording to the user device's sequence of beam selections. If the userchooses the positive increment twice in succession, the base stationincreases the increment size by one unit and then adjusts the beam byadding the updated increment to the baseline direction. That is, thebase station escalates the increment size responsive to repeatedsame-sign requests, and de-escalates it upon a reversal in sign. In thisexample, the base station follows a linear escalation, such as a “1, 2,3, 4” size sequence, as long as the user device continues to request thesame sign (positive or negative) increment. However, if the user devicethen reverses by selecting the opposite sign, the base station reducesor de-escalates the increment size by 1 unit, as in a “4, 3, 2, 1”sequence. The maximum increment size is 4 and the minimum is 1. Inaddition, whenever the user device requests no change in the beamdirection, the base station resets the increment size to 1.

There are two exceptions to the above rules, in the depicted example.First, the base station avoids returning to an already-tested beamdirection, unless the increment size has been reduced to the minimumsize. This avoids instabilities. If the user device selects a testsignal that would return the beam angle to a previously testeddirection, the base station automatically reduces the increment size tothe smallest size, and then applies the small increment in the requesteddirection. The base station thereby avoids repeating a previously testeddirection, unless the increment size is already at its smallest. Apreviously-tested beam direction can be repeated, but only when the userdevice explicitly requests it, such as when some condition has changed.However, if sufficient time has elapsed since the last time thatparticular beam direction was tested, then the record may be erasedbecause conditions may have changed over time. The amount of timerequired for the previous test of the particular beam direction to “gostale” depends on convention and/or on how rapidly the interferencechanges significantly. This may be monitored by measuring the backgroundinterference versus time and frequency.

The second exception is invoked when the user repeatedly alternatesbetween positive and negative increment requests (“ping-ponging”). Inthat case, the base station averages the two alternating beam directionsas a compromise, and then remains at that compromise angle until theuser device requests the same sign twice in succession. Thus the userdevice can resume normal beam-angle adjustments by requesting the samesign of increment on two successive requests, at which time the basestation resumes incrementing the direction using the smallest incrementsize. The increment size is restricted to a specific range, such as 1 to3 units in this example. Each of these behaviors is illustrated in thechart.

Time is plotted horizontally and the beam angle is plotted vertically.The jagged line 540 traces the incremental changes in beam directionversus time. The optimal beam angle is indicated by a doublewide arrow541. However, the initial beam angle starts at a completely differentangle, as shown at 542. since the initial angle 542 is far below theoptimal beam angle 541, the user device is going to select thehigher-angle beam options at each feedback opportunity, until the beamangle becomes equal or close to the optimal angle 541.

At 543, the base station sends a downlink message containing three testsignals to the user device. The user device selects whichever testsignal gives the best reception, which in this case corresponds to ahigher beam angle, and sends an uplink message specifying thatselection. Responding to the feedback message, the base stationincrements the beam angle by the initial increment size, which isinitially the smallest step size of 1 unit, as indicated at 543.

At 544, responsive to another downlink message, the user device againselects the test signal with the higher angle. The user device has thusrequested the higher angle option twice in succession, and therefore thebase station increases the increment size from 1 unit to 2 units, andthen takes the step 544 as shown. The step 544 is twice as large as theone at 543 because the base station has escalated the increment sizefrom 1 to 2 units.

The resulting beam is still below the optimal angle 541. Therefore, onthe next feedback opportunity, the user device again selects thepositive increment option. The base station again escalates theincrement size, now to 3 units, and applies it by increasing the beamangle by 3 units at 545.

Unfortunately, this step has now overshot the optimum 541. Therefore, onthe next feedback opportunity the user selects a lower angle. The basestation sees that the user device has reversed the directional requests.Due to the direction reversal, the base station reduces the incrementsize from 3 units to 2 units, and then steps down at 546.

The resulting beam angle has again passed over the optimum 541, so inthe next feedback message the user device again reverses by selectingthe higher-angle option. The base station responsively reduces theincrement further to 1 unit, and applies it at 547.

The next step causes one of the exceptions to be invoked. The userdevice again reverses direction at 548 and requests a lower angle. Thebase station cannot reduce the increment size further because it isalready at the smallest size. According to one of the exceptions, areversal while the increment size is just 1 unit causes the base stationto average the two alternating values. The base station thereforeapplies a half-size increment at 548, in the requested direction, whichis now the lower angle. This brings the beam angle within at most ½ unitfrom the optimal 541, which is generally a satisfactory beam angle. Thebase station then leaves the beam angle at that compromise positionduring the subsequent interval 549, during which the user either selectsno change, or alternates between positive and negative requests, orrequests a non-zero change alternating with a zero request, all of whichare treated as requesting no change while in the compromise state.

At a later time, the optimal beam angle has suddenly changed to 550, dueperhaps to motion of the user device or a change in propagation.Consequently, the user device now selects the lower angle option twicein succession at 551. Repeated same-sign requests cause the base stationto exit the compromise state 549 and resume incremental adjustments.Therefore the base station applies a reduction in angle of one unit at551.

The user device again selects the lower beam angle at 552, which causesthe base station to escalate to a 2-unit decrement, which brings thebeam angle to 553. However, it is still not enough, so at 554 the userdevice again asks for a lower beam angle, whereupon the base stationincreases the increment size to 3 units and applies it as shown. Thisovershoots the new optimum 550, so the user device then select a higherangle on the next feedback opportunity 555.

At this point, the base station changes the rules slightly. Previously,upon a reversal, the increment size was de-escalated by only 1 unit,that is, reduced from 3 to 2 units in that case. But the base stationnoted that de-escalating by only one unit tends to cause oscillatorymotion, as in the 546-548 steps. In some systems, oscillation of thebeam angle can be wasteful, or at least inconvenient to the basestation. To avoid oscillations like that, the base station changes therules so that now, upon a reversal, the increment size is reducedsuddenly to the lowest value of 1 unit, instead of being de-escalatedgradually. Therefore at 555, when the user device reversed and beganrequesting a positive change, the base station immediately reduced theincrement size to 1 unit. That increment was then applied as shown 555.Reducing the step size to the lowest size upon a reversal is moreconservative than reducing the size gradually, by one unit at a time.Oscillations are then limited to two reversals, at which time thecompromise rule is invoked.

The base station adds the one-unit adjustment to the beam angle at 555,but the beam angle is still below the new optimum 550. Therefore, theuser device again requests the higher beam angle at 556. The twosuccessive positive requests should cause the base station to increasethe increment size to two units at that point. However, doing so wouldreturn the beam angle to a previously-tested level 553. Specifically,applying a 2-unit increase at 556 would return the beam angle to 557(dash) which equals the previously-tested level of 553, which would be awaste of time since that beam angle was recently tested and rejected bythe user device. Therefore, the base station applies another exceptionat this point by not escalating the step size at all. Instead, the basestation keeps the increment size at the smallest size, which is still 1unit. The base station then applies an increment of 1 unit at 556. Thisbrings the beam angle to 558, which is very close to the new optimum550, and therefore is satisfactory. The beam angle then remains alignedwith the new optimum thereafter, until conditions again change.

To summarize, the base station suppressed oscillations by de-escalatingabruptly to the minimum size upon each reversal. If the user devicetries to revisit a recently-tested angle, the base station also reducesthe step size to the minimum. Upon a reversal while the increment sizeis already set to the minimum, the base station averages the twoalternating beam directions and remains at that compromise setting untilthe user device requests two same-sign increments in succession.

In this example, the base station escalates the increment size first,and then rotates the beam direction by adding or subtracting the newincrement size. In another embodiment, the order of action is reversed:the base station rotates the beam along the selected test signaldirection first, and then performs the escalation of the step size. Theformer (escalate the step size first, then rotate the beam) is moreaggressive at seeking the optimal angle when the error is large, but thelatter (rotate beam first, then escalate) may reduce the incidence ofoscillation. It is a matter of convention or network preference whichaction is performed first, escalation or rotation.

FIG. 5C is a schematic showing an exemplary embodiment of an iterativeadjustment of transmission power, according to some embodiments. Asdepicted in this non-limiting example, the transmitted power 560 isplotted vertically and time is horizontal. A doublewide arrow indicatesa range of signal quality 561-562, within which the reception isacceptable. If the signal quality is below a lower threshold 561, theuser device will ask for additional power at the next feedbackopportunity. If the signal quality is above an upper threshold 562, theuser device is obligated to request less power, to avoid generatingexcessive interference.

The base station successively adjusts the transmitted power level up anddown by incremental changes, upon request by the user device, until thesignal quality is within the acceptable range 561-562. The rules in thiscase call for the increment size to start out at the lowest size,initially. When the user requests the same sign of adjustment twice insuccession, the increment size is doubled, and upon any reversal ofsign, the increment size reverts to the smallest size. Upon twosame-sign requests, the step size is doubled to 2 units, and upon thethird same-sign request it is doubled again to 4 units, that is, a“binary” escalation. Then, upon a reversal, the base station reduces theincrement size to the smallest size, 1 unit. In this case, a “reversal”includes changing from a positive to a negative request or vice-versa,or changing from zero to a non-zero request or vice-versa.

In this example, the width of the acceptable zone 561-562 is wider thanthe smallest size increment. This may avoid oscillations since any valuewithin the acceptable zone would likely remain unchanged unless externalconditions change.

Initially, at 563, the reception quality is poor because the transmittedpower is too low. Upon receiving a message at 564, the user devicemeasures the signal quality of the test signals provided with themessage, and determines that the signal quality is below the lowersignal quality threshold 561. Therefore the user device transmits afeedback message requesting an incrementally higher power level, whichthe base station applies at 564.

Later, at 565, the user device receives another message and againdetermines that the signal quality is below the acceptable range, andtransmits feedback requesting a further incremental power increase.Having received two positive requests in a row, the base station thendoubles the size of the increment and increases the power level by 2units, as shown at 565. This is still below the lower threshold 561, soat 566 the user device requests a power increase a third time, whereuponthe base station again doubles the increment size to 4 units and appliesthe power increment at 566. Thus the increment size is doubled upon eachrepeated request of the same sign.

The 4-unit increment 566 puts the power level above the upper threshold562, which causes the user device to then select a lower power level onthe next feedback. This reversal breaks the string of increases.Accordingly, the base station reduces the increment size back to thelowest 1-unit size, and applies it at 567. The last step 567 finallyplaces the signal quality between the thresholds 561-562, and thereforethe power level remains at 568.

At time 570, conditions change. Perhaps there is additional interferencefrom outside the cell, or the mobile device moves farther from the basestation, or other change. Consequently, the user device now requestsadditional power to compensate the losses, and receives a 1-unitincrease 570, followed by a second request and a 2-unit powerenhancement at 571. However, this would cause the transmitted power tobe higher than the highest transmission power limit 569 (dash) permittedto the base station. The base station cannot exceed the upper limit 569except in an emergency. Therefore, the base station decides toaccommodate the user device by changing the modulation scheme instead ofincreasing the power.

The base station then changes to a different modulation scheme. In thiscase, the base station selects a modulation scheme that provides largernoise tolerance or a longer symbol-time or both, in an attempt toprovide better reception to the user device, albeit with reducedinformation density. The modulation change is communicated to the userdevice in a downlink message 572 represented by an arrow. Because thenew modulation scheme has better noise margins and/or a longerintegration time per message element, the base station determines thatthe requested power increase is no longer necessary. Instead, the basestation calculates that the new modulation scheme is sufficientlyliberal that the power can safely be reduced, and will still providesufficient signal quality to the user device. Therefore, the basestation reduces the transmitted power to the level 573, instead ofincreasing it as requested. The base station estimates that the newpower level 573 may be suitable, given the modulation change. In thiscase, the new power level is close to the desired signal quality range561-562. Then, after slight fine-tuning at 574, the power level remainsat 575 which is between the thresholds 561-552, and the base stationcontinues to use the downgraded modulation scheme for communicating withthe user device until conditions again warrant another change.

FIG. 6A is a flowchart showing an exemplary embodiment of a method foriteratively, incrementally adjusting a transmission parameter, accordingto some embodiments. As depicted in this non-limiting example, a basestation adjusts a transmission parameter (such as power or beam angle)incrementally up or down, according to a +/0/− feedback request from auser device. The increment size is variable, but is limited to apredetermined range between a minimum and maximum increment size. Forexample, the permitted increment sizes may be 1, 2, 3, 4, 5, 6, 7, 8units. Initially, the increment size is set at 2 units in this case. Onrepeated requests for the same sign of increment, the increment size isdoubled. On a reversal from positive to negative or vice-versa, theincrement size is halved. If the user requests zero change, and thensubsequently requests a positive or negative change, then the incrementsize is returned to the initial size of 2 units.

At 601, a base station transmits a downlink message to a user deviceusing the current setting of a transmission parameter such as the beamangle or the transmission power. At 602, the base station receives afeedback message, optionally multiplexed with an acknowledgement for thedownlink message, including a +/0/− feedback indicator for thetransmission parameter. For example, the feedback indicator can have 3modulation states, such as a positive amplitude, zero amplitude, andnegative amplitude, corresponding to a selection of increasing, keepingunchanged, or decreasing the parameter setting. The user device may ormay not know the actual value of the transmission parameter beingvaried, nor which selection corresponds to which increment. The basestation is responsible for interpreting the feedback message, adjustingthe increment size, and applying the requested adjustment.

At 603, the base station interprets the feedback message and determineswhether the user device requests a positive (+) or negative (−) or zerochange (0), as indicated by a three-output interrogator. If the userdevice selects zero change, the task is done at 604.

The base station then compares the current request with the previousselection. At 605, responsive to a negative change request, the basestation doubles the increment size at 607 if the previous selection wasalso negative. The base station halves the increment size at 609 if theprevious selection was positive. The base station restores the incrementsize to the initial value at 608 if the previous selection was zero.Likewise, responsive to a positive change request 606, the base stationdoubles the increment size at 612 if the previous selection was alsopositive, or halves the increment size at 610 if the previous selectionwas negative, and restores the increment size to the initial value at611 if the previous selection was zero.

Then at 613, the base station checks whether the adjusted increment sizeis outside the max-min range, and if so, sets it to a minimum at 614 orto a maximum at 615. The base station then applies the increment at 616to the parameter, thereby adjusting the beam angle or power, and waitsfor another message opportunity.

Optionally, in dash, the base station may switch to a larger or smallermodulation scheme when the requested power reaches a predetermined powerlimits. For example, at 617 the base station can check whether therequested power level is higher than a predetermined high power limit orlower than a lower limit. If the requested power is below the lowerlimit, this indicates that reception is so good that the throughput canbe increased by selecting a larger (more bits per message element)modulation scheme at 618. On the other hand, when the requested powerexceeds the transmission capabilities of the base station (or itsregulation limits), then the base station can determine that the currentmodulation scheme is no longer suitable, and may switch to a smallerscheme 619 that has a larger separation between the modulation states,and hence a larger noise margin. If necessary, the base station may alsoswitch to a lower numerology for longer integration times, or otherstrategy to achieve satisfactory communications with the user device.

The base station and the user device, in cooperation, have adjusted abeam parameter (or multiple multiplexed parameters) upon each downlinkmessage. The incrementation selection was made according to the receivedsignal quality. The adjustments were thus accomplished in realtime, withminimal-to-negligible expenditure of resources.

FIG. 6B is a flowchart showing an exemplary embodiment of a method for auser device to provide incremental feedback, according to someembodiments. As depicted in this non-limiting example, a user devicereceives a downlink message at 651. The downlink message may includemultiple (such as three) test signals, and/or other message items whichthe user device can measure. The user device can then select one of thetest signals as the favored version. At 652, the user device transmits afeedback message to the base station indicating one or more requests,responsive to the downlink message and its associated test signals orthe like. For example, the feedback message may indicate a preferredselection of beam angles, or a request for higher or lower transmissionpower, or other transmission parameter.

The incrementation parameters can be established by convention orstandard, and can be altered as needed by the base station or corenetwork based on conditions. The increment can be changed in a linear orbinary or other escalation formula. The base station can determine thenumber of equal-sign requests that cause an escalation, and whether theincrement size is then decreased upon direction reversal and by howmuch, and whether to suppress oscillation by providing fractional-sizedincrements or a range of acceptable performance metrics or otherwise,and whether to avoid repeating previously tested values unless theincrement size is at minimum, and how to limit the range of incrementsizes, and how many increment sizes to allow, among many other variablesthat may be beneficially configured. In addition, it is the basestation's option to change to a lower modulation scheme whenever therequested power exceeds a predetermined transmission limit, and tochange to a higher modulation scheme (that is, with higher throughput)whenever the requested power drops below a lower limit (not shown).

Making changes to incrementation parameters, according to complexrealtime network conditions and user demands, is a hard problem of thetype that humans generally do poorly. AI is well suited to this type ofmultivariate optimization problem. Therefore, an artificial intelligencemodel may be developed to regulate the incrementation parameters inrealtime, as described below.

AI-Managed Incrementation

In some embodiments, the base station can use an AI algorithm todetermine which escalation rules and values to apply in incrementalfeedback adjustments. For example, an AI model can determine which setof rules to invoke depending on current conditions, and how to escalateor de-escalate the increment size, and when to apply an exception, andhow to set limits, among several other options. Such complex realtimeproblems are difficult to foresee, and difficult for humans to managewhen things happen on a millisecond time scale. Brute-force software, toaccount for the multitude of possible circumstances and incrementationoptions, would be unwieldy if not impossible. Fortunately, AI iscompetent in general to handle such problems. After being trained onactual network performance data under a variety of incrementation rulesand conditions, the AI model can manage the incrementation process toadjust beam angles, transmission power levels, and other parametersrelated to communications. The AI model must be trained on network datato seek some kind of optimization, such as overall network performanceor user experience, which may be represented by a metric for example.The AI model variables are then adjusted to provide satisfactorydecisions based on the user's incremental feedback. With such automatic,realtime control, the network may provide substantially improvedmessaging throughput, reliability, efficiency, and user satisfactionoverall.

An AI model or a machine learning routine may be employed to configurethe incrementation features and settings, as depicted in the nextfigure. For example, the optimal configuration of beam angleincrementation may depend on the number and angular distribution ofactive user devices, because if the user devices are more widely spacedapart, they are less likely to encounter interference from signalsbeamed to another user. However, the user spacing may vary substantiallyduring each day as users come and go, necessitating changes in theincrementation procedure as conditions change. Likewise the beam powerincrementation rate, and associated rules, may depend on the radialdistribution of user devices, because different distances from the basestation generally require different transmission power. In addition, theoptimal beam width may be adjusted, depending on the distribution ofradial and angular separations of those devices, yet this optimum alsovaries substantially through the day. For these and many other reasons,it may be necessary, or at least preferred, to develop an AI/ML model toadjust the incremental feedback responses.

Accordingly, an algorithm is disclosed, consisting of or derived from anAI model, which is trained by machine learning with abundant networkdata as examples. The AI model may take as input, various networkconditions, current user device distributions, externalnoise/interference, and other input parameters relevant to feedbackresponses regarding transmission. The AI model may provide as output,parameters of a suggested incremental feedback protocol, which mayinclude values such as the number of incrementation sizes, and theirvalues; whether to escalate the incrementation size in a linear orbinary or other manner; under what conditions to change the incrementsize and when to apply the increment; when to change the modulationscheme instead of incrementing, and how to compensate the transmissionparameter accordingly; and the other operational choices and issueslisted in the previous paragraphs. In one embodiment, the base stationmay use the AI model itself to select each incrementation adjustment ofeach user device's beam direction. In another embodiment however (andprobably more conveniently), the base station may use the AI model todetermine suitable rules and values for incrementation responsive toeach user device's conditions, and then allow a simpler softwarealgorithm can carry out the realtime changes according to those rulesand values. With such a model or algorithm, base stations may be able tomanage communications more efficiently and with less wasted power thatprior art adjustment schemes, while generating less background energyand providing improved reliability overall.

FIG. 7A is a flowchart showing an exemplary embodiment of a procedurefor operating an AI model to adjust the incrementation parameters of anincremental feedback procedure, according to some embodiments. Asdepicted in this non-limiting example, an AI model 702 takes as input701 a number of incrementation parameters related to a particular userdevice. The inputs may include data related to the network such as thedistribution of user devices versus distance from the base station(which affects power required) and the angular distribution (whichaffects interference), as well as the variation in active users duringeach day, or during weekends versus weekdays, among other factors. Theinputs may include data related to communications such as the networkthroughput, delays, fault rates, and traffic density among others. Theinputs may include data related to the particular user device such asits most recent feedback requests, its proximity to the base station,noise and interference experienced by that user device, and its signalprocessing and beamforming capabilities, among others. The inputs mayinclude data related to the incrementation such as the range of allowedincrement sizes, a current increment value, the user device's recentfeedback requests, and certain rules governing how the increment size isescalated and de-escalated, among others. The inputs related totransmission may include a beam power or direction or width, and amodulation scheme in which the test signals are modulated, among others.

The AI model 702 may provide as output 703 recommendations regarding theincrementation of beam angles, transmission power, or other transmissionparameters. The outputs may include the recommended size of theincrementation step, the escalation formula (such as linear or binaryescalation) and when to escalate the size, the maximum and minimumlimits of the incrementation step and of the transmission parameteritself, and when to invoke exceptions to the rules such as the no-repeatexception.

FIG. 7B is a flowchart showing an exemplary embodiment of a procedurefor preparing an AI model, according to some embodiments. As depicted inthis non-limiting example, an AI model is prepared for a base station ora user device to use in connection with incremental feedback adjustmentssuch as beam angle adjustments and beam power adjustments. In theexample, the depicted model is configured to assist the base station (orits core network) in managing an incrementation parameter such as stepsize.

At 751, software for an AI model is acquired or developed or otherwiseobtained for a supercomputer, or other processor suitable for developingan AI model. The AI model includes a plurality of inputs, a plurality ofadjustable variables, and one or more outputs that depend on the inputsand the adjustable variables. At 752, network operational data isacquired for training the AI model, such as message throughput, averagedelay, failure rate, and other network performance parameters. Theinputs may also include which parameters were in use when the networkperformance data were taken, such as the default increment size, theescalation formula (such as linear or binary) for increasing ordecreasing the increment size, rules regarding direction reversal andno-repeat exceptions, among other possible parameters of the incrementaladjustment process.

At 753, the AI model is operated to predict the future networkoperations, or a network performance metric, according to the inputnetwork parameters and the incrementation parameters. At 754, theprediction is compared with actual network performance data acquiredwhile a network operated using those incrementation parameters. At 755,the accuracy of the prediction is evaluated. If the prediction isdetermined to be not satisfactory (that is, the predictions failed toagree with the actual network performance after the incrementation), themodel variables are adjusted at 756 to improve the predictive power, andthe flow then returns to the prediction and comparison steps for furtherrefinement. If, however, the prediction is deemed satisfactory(predictions based on the input data largely agreed with subsequentnetwork performance), the flow returns to 752 to repeat the procedurewith a different set of network and incrementation parameters. Theapplicability and accuracy of the AI model are thereby broadened foreventual field use.

At 757, the predictions are determined to be satisfactory for a varietyof network and incrementation conditions. Then the AI model, or analgorithm derived from it, is prepared for field use by a base stationor a core network.

At 758, as indicated by a doublewide arrow, the AI model, or a trimmedversion of it, or an algorithm derived from it, is installed in a basestation or a core network. The purpose of the fielded AI model oralgorithm is to determine the incrementation parameters most suitablefor use according to the current operating conditions of that network.Alternatively, or in addition, the fielded AI model or algorithm may beconfigured to predict changes in network performance consequent toproposed changes in the incrementation parameters. If the networkaccumulates enough experience with the AI model, and develops trust inits predictions, the network may decide to allow the AI model toautonomously adjust the incrementation parameters in realtime, insteadof having humans interpret the AI results and apply the changesthemselves. Direct AI management of operations may thereby enablerealtime optimization of parameters without human involvement, otherthan light supervision.

At 759, the base station (or core network) receives a feedback messagefrom a user device requesting an incremental change to the power orangle of the transmission beam toward that user device. At 760, thefeedback request and the current network conditions are provided to theAI model (or algorithm, etc.), which then selects the most suitable typeof incrementation to apply (such as linear or binary escalation), andthe incrementation values (such as initial, minimum, and maximumincrement size), and exceptions (such as no-repeat) among many otherpossible parameters of the incrementation procedure. The parameters maybe customized for each beam parameter (power, angle, width, etc.) andfor each user device in the network.

At 761, the transmission beam is adjusted according to the AI outputsand the requested change, and the flow returns to 759 for the nextfeedback message. As mentioned, that adjustment may be done by the modelor by a human interpreting the model outputs.

Optionally, at 762 (dash) the base station (or core network) can attemptto adjust the AI model variables in the field unit, based on the resultsobserved by that base station. To assist the base station in choosingthe right variables to vary, the supercomputer that developed the AImodel may have determined which variables are associated with varioustypes of faults, or a particular network condition, or a commonincrementation problems. With that knowledge, the base station can thenvary the AI variables, in the base station's field version of the AImodel, with knowledge of which variables affect the problem in question.Thus each base station can adapt its version of the AI model to betterrespond to the base station's own situations. However, if those changesresult in worse network performance, the base station or core networkcan reverse adjustments and return to the previously configuredparameters, or perhaps adjust the variables in the opposite sense, orother strategy as seems appropriate to the base station. With knowledgeof which variables can be altered to affect which types of problems, thebase station can iteratively adapt the fieldable AI model to thespecific current conditions in realtime, based on the types of feedbackreceived and current network conditions, and thereby improve thepredictions under those conditions.

Optionally, at 763, the base station (or core network) can prepare anoperational history of the feedback requests, output recommendations ofthe model, the variables adjusted by the base station if any, and theresulting network performance changes. The base station or core networkcan then send the operational history back to the AI model developer.The supercomputer can then take the operational history as new fielddata for further refinement of the AI model. Collaborative modelrefinement, between the model developer and the model user, can resultin rapid adaptation of the AI model for real-world networkingapplications.

Modulation Schemes for Feedback

The following figures show certain modulation schemes configured toefficiently transmit feedback messages, along with prior art schemes forcomparison.

FIG. 8A is a schematic showing an exemplary embodiment of a 16QAMconstellation chart, according to prior art. As depicted in thisnon-limiting example, a 16QAM constellation chart includes 16 allowedstates 801, each allowed state 801 configured as two orthogonalamplitude-modulated sinusoidal signals termed the I-branch(horizontally) and the Q-branch (vertically). Each branch is amplitudemodulated according to a set of predetermined amplitude levels, in thiscase −3, −1, +1, and +3 arbitrary units. The central cross shaperepresents zero amplitude.

FIG. 8B is a schematic showing an exemplary embodiment of a QPSKmodulation constellation chart, according to prior art. As depicted inthis non-limiting example, QPSK includes four allowed states 811 whichhere are modulated with I and Q branches equal in magnitude, at themaximum branch amplitude of ±3 arbitrary units. Other QPSK schemes arerotated by 45 degrees. An advantage of QPSK is that a receiver candemodulate a message transmitted in QPSK without calibrating theamplitude scale, as long as the receiver is time-synchronized with thetransmitter. The states 111 differ in phase only, so the receivedamplitude (which depends on many unknowns) is not relevant todemodulation with this modulation scheme. Many system-information andcontrol messages are transmitted in QPSK for that reason, when noamplitude calibration or demodulation references are present or needed.However, QPSK delivers only two bits per message element, and hencethose messages tend to occupy large regions of the resource grid.

FIG. 9A is a schematic showing an exemplary embodiment of a 9QAMconstellation chart, according to some embodiments. As depicted in thisnon-limiting example, nine allowed states 921 are each configured withan I-branch and a Q-branch, and each branch is amplitude-modulatedaccording to three predetermined amplitude levels, which in this caseare +3, 0, and −3 arbitrary units. Thus the non-zero predeterminedamplitude levels have the same amplitude as the maximum positive ornegative amplitude levels of 16QAM and QPSK. In addition, there arestates with zero transmission in one or both branches, which thestandard modulation schemes lack. As mentioned, the receiver cangenerally discern a zero-power branch from a full-power branch, and candiscern positive amplitude from negative amplitude, without havingcalibrated the amplitude scale. Hence a receiver can demodulate the 9QAMmessage natively, without calibrating the predetermined amplitude levelsand without a prior demodulation reference. (There may be an exceptionin case of an extremely weak signal, so weak that the base stationcannot reliably discriminate a zero amplitude from a non-zero amplitude.In such cases, however, then the SNR is likely so bad that communicationis impossible anyway. Therefore such cases are ignored herein.)

Also shown are headings indicating what each of the predetermined branchamplitude levels may represent. Each branch may encode a differentparameter such as incremental feedback of the beam angle or power. Forexample, the I-branch amplitudes may represent a request for an increaseor decrease in transmission power, or a request to leave thetransmission power unchanged. In the depicted case, +3 means to increasethe power by some predetermined increment, −3 means to decrease it, and0 means to leave it unchanged. In a similar way, the Q-branch levels mayindicate which of three candidate beam directions provide the bestreception, which are represented as the left, middle, and right beams orthe first, second, and third test signals. Thus the receiver can requesta transmission power adjustment and a beam alignment adjustment, allmultiplexed in a single resource element, and which is demodulatablenatively without an amplitude calibration. The base station, uponreceiving the feedback resource element, can extract the I and Qamplitudes, determine whether the user requests a change in power orbeam angle, and can then incrementally adjust the power or beam angleaccordingly. Such an incremental adjustment, provided by the basestation for each downlink message, may be more timely and less costlythan the complex messages normally used for alignment and power control.

The base station is responsible for interpreting the feedback choicesand adjusting the transmission beam accordingly. The user device may notknow, or care, which transmission parameter is involved, nor whichcandidate values of those parameters appear in which test signals. Inthis case, the user device indicates which test signal has the bestsignal quality or received power, and the base station is responsiblefor responding accordingly. For example, the downlink data message mayinclude three test signals, each directed in incrementally differentbeam directions. A first test signal may be transmitted in the samedirection as the data message, and the other two test signals may bedirected that direction plus or minus an angular increment (such as,say, ±10 degrees). The user device can then compare the signal qualityin the three test signals, and can select which test signal had the bestsignal quality or received power, and can indicate that choice in theQ-branch of the feedback message, as shown. The base station can thenadjust the beam direction as requested.

In some embodiments, the first test signal/demodulation reference isconcatenated with the start of the message. The message is transmittedwith the same beam as the first test signal. The other two test signalsare concatenated with the end of the message, and are aimed in slightlyhigher and lower beam angles. The user device can then select which ofthe three test signals provides the best signal quality, therebyproviding feedback on beam angle, and can also demodulate the messageaccording to the leading test signal/demodulation reference. Inaddition, the user device can measure the signal quality of the besttest signal, thereby determining whether additional transmission poweris needed, and can include feedback on the transmission power along withthe feedback on beam angle. Hence the test signals are doing tripleduty: demodulation reference, beam alignment test signal, andtransmission power test signal.

In some embodiments, each test signal may be a short-form demodulationreference configured to indicate modulation levels from which all theothers can be calculated. For example, the test signal may occupy asingle resource element in which the I-branch is amplitude modulatedaccording to the maximum predetermined amplitude level of the modulationscheme and the Q-branch is the minimum amplitude level. The first testsignal then serves as a beam-angle test signal and as a demodulationreference. For example, the receiver can use the test signal torecalibrate the predetermined amplitude levels of the modulation schemeof the message associated with the leading test signal. In addition, thetrailing test signals, transmitted differently from the messageelements, may still serve as phase tracking reference signals forrefining the synchronization. Since the test signals are concatenatedwith the message, the recalibration is “fresh” and may result inimproved noise cancellation.

An advantage of 9QAM, as a modulation scheme for the feedback message,may be versatility, since the user device can multiplex two incrementaladjustment requests within a single 9QAM resource element, as shown. Thebase station can then separate the feedback message into the I and Qbranches, extract the two different types of feedback information, anddetermine which option, of the three options, is requested for eachbranch. Another advantage may be reliability, since the receiver isgenerally able to discriminate between positive, negative, and zerosignals with high reliability, even without a demodulation reference,and without calibrating the amplitude. In contrast, modulation schemesthat have 4 amplitude modulation, such as 16QAM, generally require ademodulation reference for amplitude calibration. Another advantage maybe that 9QAM provides slightly over 3 bits per message element, asopposed to 2 bits with QPSK, and hence a message modulated in 9QAM isshorter than the same information modulated with QPSK. (All of theseadvantages assume, as mentioned, that the receiver has beentime-synchronized with the transmitter, and that sufficient SNR ispresent.)

FIG. 9B is a schematic showing an exemplary embodiment of an 8QAMconstellation chart, according to some embodiments. As depicted in thisnon-limiting example, 8QAM includes states 931 similar to 9QAM of theprevious figure, but without the central 0,0 state. Thus all of theallowed states in 8QAM include power transmitted in at least one of theI or Q branches. 8QAM may be preferable when there is a choice among 8items, such as 8 angles (θ₁ etc. as shown), since it provides 45 degreesof phase separation between adjacent states. 8QAM provides exactly 3bits per message element, and thus messages will be ⅔ as long relativeto QPSK.

FIG. 10A is a schematic showing an exemplary embodiment of a polar plotof an amplitude-phase modulation scheme, according to some embodiments.As depicted in this non-limiting example, states 1001 of a modulationscheme are modulated using amplitude modulation multiplexed with phasemodulation, as shown. Amplitudes are represented by circles 1002, 1003and by a radius vector 1004, while phase is represented by an angle1005. The modulation scheme includes nine states, each modulated inamplitude and phase. The nine states 1001 include four states with alarge amplitude 1002 (such as five arbitrary units), four states with asmaller amplitude 1003 (such as three arbitrary units), and a centralstate with zero amplitude. As in the previous example, theamplitude-phase modulated states 1001 can encode two multiplexedfeedback parameters, such as transmission power and beam angle. Forexample, state 1006 with the maximum amplitude level 1002, and a phaseangle of 45 degrees, may represent a request to increase the power byone increment, and to prefer the second demodulation reference. That is,the resource element is modulated to indicate the power feedback in theamplitude, and the beam angle feedback in the phase, simultaneously. Ina similar way, state 1007 may request a decrease in power and no changein angle. As with the previous example, the central state 1008 mayindicate no change in the power or beam direction.

Each state 1001 in the depicted example has a full 90-degree phaseacceptance region, since all the states are separated from theirequal-amplitude neighbors by 90 degrees. Therefore the modulation schemeis able to accommodate substantial phase noise without faulting. 16QAM,and other prior-art schemes carrying more than 2 bits, generally do nothave that feature, because some of their states are closer together than90 degrees. As a further advantage of 9-state amplitude-phasemodulation, the large-amplitude states 1002 are spaced apart in phase by45 degrees from the closest small-amplitude states 1003, and vice-versa.This large separation in both phase and amplitude thereby providesadditional noise margins, for unambiguous demodulation despitesubstantial amplitude noise and phase noise. As with 9QAM, the 9-stateamplitude-phase modulated states can provide incremental feedback on twodifferent transmission parameters in a single resource element. In thiscase, the two parameters are the beam power and beam angle. As a furtheradvantage, the 9-states amplitude-phase modulation states, without anamplitude calibration, correspond to an 8PSK modulation scheme (45degree phase separations) plus the central 0,0 state. Therefore, thereceiver can demodulate the 9-state amplitude-phase states natively,without an amplitude calibration, by measuring the phase.

The base station or the user device can determine whether to use 9QAM orthe 9-state amplitude-phase modulation scheme for compact feedbackmessages depending on reception, interference, and network conditions.For example, the base station or the user device can include analgorithm, derived from artificial intelligence using machine learning,to select the most appropriate modulation scheme. For example, if phasenoise is high enough to cause excessive phase faults, the AI-derivedalgorithm can recommend switching to the amplitude-phase modulationscheme instead, for improved phase noise tolerance. This option may beespecially important at high frequencies.

An advantage of 8/9QAM may be that there may be no need for ademodulation reference. The receiver may be able to unambiguouslydemodulate resource elements modulated in 8/9QAM without firstdetermining the amplitude levels of the modulation scheme. Demodulationreferences are required for accurate amplitude demodulation, but if thetask is merely to determine whether a signal is positive, negative, orzero, there may be no need for a calibration. Most receivers are capableof demodulating QPSK, which does not require a demodulation referencebecause the amplitude is not a modulation parameter, and the phase isreadily recognized after synchronization. In the same way, receivers maybe able to demodulate 8/9QAM based on the presence or absence of signal,and whether it has positive or negative sign, regardless of themagnitude. Thus a receiver capable of demodulating QPSK natively at 2bits per message element should be able to demodulate 8/9QAM or8/9-state amplitude-phase modulation natively, for the same reason.

8QAM carries 3 bits per message element, as does the 8-stateamplitude-phase modulation scheme. Higher information density may bepossible, even in difficult noise/interference environments, usingasymmetric modulation. For example, asymmetric modulation schemes thatmay be advantageous include 3×4QAM and 5×3QAM. Alternatively, if a largephase-noise tolerance is required, amplitude-phase modulation schemeswith 9 or 17 allowed states are available, and which generally resistphase faults much better than any QAM version, as detailed below.

FIG. 10B is a schematic showing an exemplary embodiment of a polar plotof an 8PSK modulation scheme plus a zero-power state, according to someembodiments. As depicted in this non-limiting example, a modulationscheme that includes 8PSK states 1011 plus a central zero-power state1018 can transmit over three bits of information per message element, ortwo multiplexed feedback parameters each with +/0/− selectivity, withouta prior amplitude calibration. The 8PSK states 1011 are generallytransmitted with the same amplitude 1012 and with a phase separation of45 degrees, while the central state has no power. An advantage of thismodulation scheme may be that it carries 50% more information densitythan QPSK while still providing a substantial phase margin. Anotheradvantage may be that the zero-power state can reveal externalinterference, including measuring the amplitude and phase of theinterference if it is large enough.

Thus the base station can choose between a variety of modulation schemesincluding quadrature amplitude modulation with eight (FIG. 9B) or nine(FIG. 9A) states, or multiplexed amplitude-phase modulation with eightor nine states (FIG. 10A), or 8-state phase-shift keying plus a zeropower state (FIG. 10B), among others disclosed below.

When combined with an acknowledgement for each downlink message, themultiplexed incremental feedback disclosed above can keep the basestation's beams aligned in realtime, for each user device individually,at an insignificant cost compared with the message-heavy feedbackprotocols of the prior art.

FIG. 11A is a constellation chart showing an exemplary embodiment of anasymmetric QAM modulation scheme, according to some embodiments. Asdepicted in this non-limiting example, an asymmetric 3×4QAM modulationscheme includes 12 allowed states 1101 distributed across three I-branchamplitudes (here +3, 0, −3 arbitrary units) and four Q-branch amplitudes(+3, +1, −1, −3 units). Guidelines are shown dotted. The central crossshape represents zero amplitude. The pattern has rectangular symmetrysince a 180-degree rotation reproduces the pattern, but not squaresymmetry because a 90-degree rotation does not. The scheme providesalmost 3.6 bits per message element.

The depicted modulation scheme may be advantageous in applicationswherein feedback selections are to be communicated for two differentparameters, all in a single multiplexed message element. For example,the feedback may include selecting one of three power levels (such asincrease, decrease, and stay the same), and selecting one of four beamangles. Using the 3×4QAM arrangement of states, the user device canspecify its feedback request for both beam parameters in a singleresource element, according to some embodiments.

FIG. 11B is a constellation chart showing an exemplary embodiment ofanother asymmetric QAM modulation scheme, according to some embodiments.As depicted in this non-limiting example, an asymmetric 5×3QAMmodulation scheme with five I-branch amplitude levels (+3, +1.5, 0,−1.5, −3 units) is multiplexed with three Q-branch amplitude levels (+3,0, −3 units), generating 15 allowed states 1111. This delivers 3.9 bitsper message element, almost the same as 16QAM, but tailored to amultiplexed feedback message involving two different parameters with aone-of-three selection multiplexed with a one-of-five selection, in thisexample.

As an alternative, the 5×3QAM scheme may be provided by selecting just15 states of a larger scheme, such as 25QAM. The remaining ten statesmay be used for some other purpose. An advantage of a one-of-fiveselection may be that a feedback parameter can be specified with moreversatility than a one-of-three choice. For example, the five optionsmay represent a request of no change (0), a small positive or negativechange (±1.5), or a maximal positive or negative change (±3). Whilethese requests can be represented by a larger standard modulationscheme, such as 64QAM, the level spacing of 25QAM (and therefore thenoise margins) are generally larger than for 64QAM. Hence thenon-standard QAM modulation schemes disclosed here, including with zerobranch states, or a zero central state, or odd numbers of predeterminedbranch amplitude levels, or different numbers of levels in the twobranches, may be considered for particular applications requiring suchbenefits.

FIG. 12A is a polar plot showing an exemplary embodiment of anamplitude-phase modulation scheme with 9 states, according to someembodiments. As depicted in this non-limiting example, anamplitude-phase modulation scheme includes states that are bothamplitude modulated and phase modulated. There are no branches. In thisexample, each allowed state 1246 is amplitude modulated according tonon-zero levels 1242, 1243, 1244, 1245 as well as a zero-amplitude state1241. The phase modulation levels are 0, 90, 180, 270 degrees (oralternatively, the same rotated by 45 degrees). Each state 1246 isseparated from the other equal-amplitude state by 180 degrees, asindicated at 1248. In addition, each state is separated from the nearestequal-phase state by two amplitude levels. In addition, each state isseparated from other states by 90 degrees in phase plus one amplitudelevel as indicated at 1247. Due to the relatively large phaseseparations between states, the 9-state amplitude-phase modulationscheme can provide a high degree of phase-noise tolerance. In addition,the amplitude-noise tolerance is also high, due to the multipleamplitude levels between equal-phase states. The scheme providesslightly over 3 bits per message element.

In some applications, the zero state 1241 may be problematic since itcontains no detectable signal. In those applications, the remaining 8states can be used instead, thereby providing 3 bits per messageelement. In that case, the central zero-power state may represent aspecial function such as demarking the start and end of a message.Messages modulated according to this amplitude-phase modulation schemecan then be ⅔ as long as with QPSK, while providing 2.4 times higherphase-noise immunity than 16QAM due to the larger phase separationbetween states.

Wireless receivers that process QAM transmissions can also process anddemodulate messages modulated according to the depicted amplitude-phasemodulation scheme using simple arithmetic. In one embodiment, thereceiver can receive the message as an OFDM symbol, extract each messageelement according to its subcarrier frequency, extract the I and Qbranches according to phase, measure the branch amplitudes, and thencalculate the sum-signal amplitude and the sum-signal phasemathematically. For example, the sum-signal amplitude is the square rootof a sum of the squares of the branch amplitudes, and the sum-signalphase is the arctangent of one branch amplitude divided by the other.Hence the receiver can determine the modulation state of each messageelement modulated in 9-state amplitude-phase modulation, by receivingand signal-processing according to orthogonal branch signals, and thenconvert mathematically to the sum-signal amplitude and sum-signal phasefor demodulation, thereby obtaining large noise margins in amplitude andphase.

FIG. 12B is a modulation table showing an exemplary embodiment of anamplitude-phase modulation scheme with 9 states, according to someembodiments. As depicted in this non-limiting example, the states 1256(corresponding to the states 1246 of the previous example) are nowplotted in a modulation table with phase horizontally and amplitudevertically. As mentioned, each state 1256 is separated from the nearestequal-amplitude state by 180 degrees as indicated at 1258, and from thenearest state in an adjacent amplitude level by 90 degrees at 1257. Thezero-amplitude state 1251 is arbitrarily placed at the 90-degreeposition, although the phase of a zero-amplitude transmission isactually undefined. As mentioned, if a particular application isincompatible with the zero-amplitude state 1251, the user device candiscard it or use it for special purposes. Using only the other 8 states1256, the user device can then obtain an information density of 3 bitsper message element, while retaining the superior phase-noise marginsand amplitude-noise margins of the amplitude-phase modulation schemes.

FIG. 13A is a polar plot showing an exemplary embodiment of anamplitude-phase modulation scheme with 17 states, according to someembodiments. As depicted in this non-limiting example, the 17 states1366 are distributed on five amplitude levels 1361, 1362, 1363, 1364,and 1365, and eight phase levels of 0, 45 degrees, 90 degrees, and soforth. The central state 1361 has zero amplitude.

The 17-state amplitude-phase modulation scheme has many advantages. Oneadvantage may be that it can provide over 4 bits per message element,thereby matching 16QAM, plus the central zero state 1361. Anotheradvantage may be that the phase separation 1368 of equal-amplitudestates is at least 90 degrees for every pair of equal-amplitude states,thereby providing wide phase-noise margins. Another advantage may bethat the amplitude separation of all equal-phase states is two amplitudelevels (except for the zero state 1361, which has undefined phase),which thereby provides additional amplitude-noise margins. Anotheradvantage may be that the phase separation of any two states that differin amplitude by one level is at least 45 degrees as indicated 1367.Another advantage may be that the zero-amplitude state 1361 can providea measure of the background noise whenever it occurs in a message, whichcan thereby enhance message reliability and noise compensation. Hencethe modulation scheme can provide information density that at leastequals 16QAM, while also providing superior amplitude noise margins andsuperior phase noise margins, at negligible cost.

As mentioned, transmitters can employ the depicted amplitude-phasemodulation scheme for transmission, and receivers can process thereceived signal by separating orthogonal I and Q branches as usual. Thereceivers can then demodulate the signals according to theamplitude-phase modulation scheme by calculating the sum-signalamplitude (square root of sum of the two branch amplitudes squared) andthe sum-signal phase (arctangent of the Q amplitude divided by the Iamplitude), and can thereby recover the high noise margins, in bothamplitude and phase, at no cost other than the simple sum-signalcalculations listed.

FIG. 13B is a modulation table showing an exemplary embodiment of anamplitude-phase modulation scheme with 17 states, according to someembodiments. As depicted in this non-limiting example, the states 1376of an amplitude-phase modulation scheme correspond to the states of theprevious example, but now plotted with phase horizontal and amplitudevertical. The zero-amplitude state 1371 is arbitrarily plotted at aphase of 45 degrees. The phase separation between all equal-amplitudestates is 90 degrees as indicated 1378, and the phase separation betweenstates differing by one amplitude level is 45 degrees as shown 1377. Theamplitude difference between two equal-phase states is two amplitudelevels, as indicated 1379. Hence the amplitude-phase modulation schemecan be expected to provide noise margins substantially superior to thoseof 16QAM, while at least matching the message throughput of 16QAM.

AI for Modulation Management

Due to the many competing parameters and the many available modulationschemes, the base station may employ an AI model to assist indetermining whether conditions warrant changing modulation schemes forcommunicating with a particular user device, and which modulation schemeto switch to. Examples below illustrate such an AI model.

FIG. 14A is a chart showing an exemplary embodiment of an AI modelconfigured to select a modulation scheme, according to some embodiments.As depicted in this non-limiting embodiment, the AI model can takevarious network conditions and user requests as input, and provide asoutput a recommendation of a more suitable modulation scheme.

At 1401 the base station (or other network entity) provides variousinput parameters to an AI model tasked with selecting a preferredmodulation scheme. The inputs may include data about availablemodulation schemes such as 8/9QAM, 8/9-state amplitude-phase modulation,asymmetric schemes and schemes with odd numbers of predeterminedmodulation levels, and schemes with a zero-power state or a zero-powerbranch, among others. The inputs may include data about the user devicesuch as the QoS required, the current signal quality of downlinkmessages, rates of various fault types, delays experienced by the userdevice, capabilities of user devices, among others. The inputs mayinclude data about the network operations such as the current traffic inmessages or bits per second, number of current active users,interference and error rates, among others.

At 1402, the AI model digests the input data according to certaininternal functions and adjustable variables, which have been tuned usingabundant prior network data, continuing until the AI model can provideuseful output recommendations.

At 1403, the AI model specifies a preferred modulation scheme. The AImodel may know what the current modulation scheme is, and then maysuggest differences between the preferred and current schemes, such asincreasing the phase margins or decreasing the number of amplitudelevels, for example. Alternatively, the AI model may be unaware of thecurrent modulation scheme, and then may provide the recommendationswithout prejudice. The outputs may also recommend changing from a QAMmodulation scheme to an amplitude-phase “A-P” modulation scheme, foradditional phase margins to combat frequent phase faults, or to changeto an asymmetric modulation scheme with a different number of amplitudelevels and phase levels, or to switch to a higher modulation level toincrease throughput if the current interference level is low. Forincreased versatility, the AI model may recommend a modulation schemewith states having one zero-amplitude branch, or a state with zeropower. These states may be used for message encoding, or special usessuch as demarking the start and end of each message.

FIG. 14B is a flowchart showing an exemplary embodiment of a method forselecting a modulation scheme using AI, according to some embodiments.As depicted in this non-limiting example, a base station uses an AImodel to select a new modulation scheme based on feedback from a userdevice and network conditions.

At 1411, the base station receives a feedback message that indicateswhich test signal, of a plurality of downlink test signals, provides thebest signal quality. The feedback message also indicates whether theuser device requests additional transmission power or less transmissionpower, among other things. At 1412, if not sooner, the base stationmeasures one or more network operation parameters such as the currentthroughput and fault rate, average delay or latency per message, andoptionally certain detailed performance criteria such as whether faultsare primarily phase faults or amplitude faults. The base station alsochecks the QoS requested by this user device, and especially noteswhether that required QoS is not being fulfilled.

At 1413, the base station (or core network or other network asset)provides the user data and current conditions data to an AI modeltrained on network performance data, taken with various networkconditions and modulation schemes. The base station then operates the AImodel at 1414 to select a preferred modulation scheme. If the preferredmodulation scheme is the same one currently in use for this user device,then the flow returns to the beginning as indicated by a dashed arrow.In this example, however, it is assumed that the preferred modulationscheme is different from the one currently in use, so a change may berecommended.

At 1415, the base station also evaluates the costs and benefits ofchanging the modulation scheme. The base station may operate the same AImodel, or a different AI model, or an algorithm that may or may not bederived from AI, to estimate the costs of changing the modulation schemefrom the current modulation scheme to the preferred modulation scheme.The costs may be calculated for this particular user device or for theentire network to make the change. The cost may be evaluated accordingto the extra messaging required to inform the user device of the change,the interruption and delay introduced to the user device by such achange, as well as the intended beneficial consequences of making themodulation change. Some of these may be estimates.

At 1416, the base station determines whether the benefit of making thechange is greater than the cost, and if so, at 1417 the base stationinforms the user device, and then (after receiving an acknowledgement)the base station and user device make the change in modulation scheme atthe same time. The flow then returns to the beginning.

Optionally, in dash at 1418, the base station can determine whether theuser device has requested an increase or decrease in transmission power,to bring the received signal quality into agreement with a predeterminedrange. If so, and if the base station changes the modulation scheme,then the base station may decide that the new modulation scheme issufficiently effective that the transmission power can be reduced,instead of increased. Therefore the method shows, at 1419, the basestation adjusting the power (or other transmission parameter) up or downaccording to the properties of the new modulation scheme, calculated toprovide the QoS level that the user device has requested, but with thenew modulation scheme.

Initial Access Feedback

The following examples show how a base station can align its beam towarda newly arriving user device, as early in the initial access process aspossible. For example, the SSB (synchronization signal block) messagemay be transmitted once isotropically instead of multiple times atdifferent directions, but with a set of embedded test signals, each testsignal transmitted in a different direction. The user device candetermine which test signal is best received, and then communicate thatchoice to the base station later, such as during the random accesspreamble or one of the initial access messages, for example.

FIG. 15A is a schematic showing an exemplary embodiment of a systeminformation message including multiple angular transmissions, accordingto some embodiments. As depicted in this non-limiting example, asynchronization signal block SSB message 1500 includes a primarysynchronization signal PSS, a secondary synchronization signal SSS, anda distributed physical broadcast channel PBCH message. The SSB occupies4 symbol-times across 240 subcarriers totaling 3.6 MHz bandwidth at thelowest numerology, or 960 resource elements including some unassignedareas. In contrast, prior-art access procedures call for the SSB to betransmitted numerous times in different directions, which consumes hugeamounts of bandwidth and transmission power.

Advantageously, the depicted SSB message is transmitted just once,isotropically, per 20 msec interval. Prospective user devices canreceive it from positions all around the base station. For localization,the depicted SSB includes a number of test signals in one of theunassigned regions 1501, each test signal transmitted in a differentdirection. For example, the test signals can be inserted in the first 56resource elements of the first symbol-time. Those resource elements areunassigned and therefore do not overlap with the primary synchronizationsignal or other parts of the SSB. The test signals thereby enable theuser device to determine its direction relative to the base stationbased on the received amplitude or power of the test signals. It is notnecessary to pre-calibrate the amplitude scale because the measurementsare relative. It is not necessary to pre-synchronize with the basestation because the best direction should be clearly revealed in theamplitude of the raw waveform (and in the sum-signal, if analyzedaccording to orthogonal branches). It is not necessary for the userdevice to have a beamforming capability since it is merely determiningthe amplitude of a received signal (although user devices withbeamforming can adjust their transmission beams according to the angleof the best-received test signal, without further cost).

In addition, the latitude and longitude of the base station may also beadded to a second unassigned region 1502, for further localization. Thusthe prospective user device can obtain the SSB information, and alsodetermine the optimal downlink beam direction, by measuring which of thetest signals 1501 provides the best signal quality. The user device canthen indicate that preference in a feedback message, which may be addedto one of the initial access messages. The base station can thentransmit to the user device using a directed beam thereafter.

In some embodiments, the 56 test signals are transmitted in 56 narrowtransmission beams, each beam 6.4 degrees wide, thereby enabling theuser device to specify its angle within 6.4 degrees all around the 360degree circle. In another embodiment, each beam can be 8.5 degrees wide,overlapping each of the neighboring beams by 2.1 degrees. Then the userdevice can specify in the feedback message whether the best-receivedsignal was in one of the test signals or in a pair of adjacent testsignals, thereby indicating whether the user device is located in aregion dominated by one of the directed beams or in an overlap region.The user device can thereby indicate its location within about 2.1 or2.2 degrees. Such finesse may be needed in future high-density,high-frequency environments to minimize background generation andunwanted interference. Determining the downlink beam with high precisionearly in the initial access process, such as upon the first message, maythereby enable efficient use of the available spectrum.

In summary, the prior-art SSB message is generally transmitted manytimes in different directions, whereas the disclosed method has the SSBtransmitted only once per cycle, and includes numerous test signals inthe SSB for directionality. The SSB (other than the test signals) isbroadcast isotropically, and each test signal is transmitteddirectionally, in a different direction. Thus the simplified SSB schemeaccomplishes user localization, with vastly less transmitted power andresource usage than the prior scheme.

The figure also shows several identical signals 1503, transmittedtime-spanning after the SSB 1500 in a single subcarrier, such as thefirst subcarrier of the SSB. The identical signals 1503 are alltransmitted isotropically, in this example, at the same frequency andpower. User devices that have a beamforming capability can then aligntheir reception beam toward the base station by varying their receptionbeams during the identical signals 1503, and can thereby find the bestreception beam angle.

An advantage of providing the beam test signals 1501 in the SSB 1500 maybe that the prospective user device can determine the optimal beam anglefor reception, before making contact with the base station, by measuringamplitudes of the received test signals 1501 with various reception beamdirections. Another advantage may be that the user device may be able toselect the best test signal 1501 without a prior demodulation reference,since the user device can measure the amplitude or signal quality of theraw signals without demodulating them and prior to synchronization.Another advantage may be that the user device can transmit a feedbackmessage later during the initial access messaging sequence, indicatingwhich of the test signals was best received. Another advantage may bethat the SSB can be broadcast omnidirectionally (except for the testsignals), and hence can be transmitted just once per cycle.

There may no longer be a need to use up large amounts of bandwidth andpower by transmitting the entire SSB sequentially in multipledirections. For example, the user device can determine the beamselection, according to the best-received test signal, and can indicateso to the base station in a feedback message, or by another codeembedded in or appended to the RACH preamble, or appended to one of theother initial access messages, or some other uplink message followingthe initial access procedure. Optionally, the feedback may also includea power adjustment request based on the amplitude of the best-receivedtest signal 1501, which may further streamline the initial accessprocedure.

After receiving the SSB, the user device with beamforming capability candetect several uniform signals 1503 following the SSB, in this casetime-spanning, while varying the reception beam direction or width. Thusthe user device can determine the direction toward the base station forits own use.

As another alternative, the base station may use a formula or table totransmit each test signal 1501 in a predetermined direction, and theuser device may know that relationship. Then the user device candetermine the direction of the base station, relative to the userdevice, by adding or subtracting 180 degrees to the angle of thebest-received test signal, and thereby determine the direction towardthe base station for uplink messages.

As a further alternative, the base station can transmit a locationmessage to the user device after receiving a feedback message, thelocation message indicating to the user device the directioncorresponding to the selected test signal 1501. The user device can thendirect its uplink transmission and downlink reception beams in thatdirection plus 180 degrees.

FIG. 15B is a flowchart showing an exemplary embodiment of a procedurefor aligning beams prior to initial access, according to someembodiments. As depicted in this non-limiting example, a user device candetermine its direction relative to the base station based on testsignals in the SSB message, and then transmit a feedback messageconcatenated with the random access preamble.

At 1511, the user device receives the SSB message of a base station, andat 1512 measures the amplitude or received power or signal quality ofeach test signal embedded in the SSB. The user device then receives anSIB1 message 1513, and at 1514 transmits a random access preamblefollowed by a feedback message to the base station. The feedback messagemay be modulated in a modulation scheme, such as 8QAM or 9-stateamplitude-phase modulation, that does not require a prior amplitudecalibration. Alternatively, the feedback message may be transmitted withthe same modulation and timing as the preamble. In either case, thefeedback message indicates which of the test signals provided the bestsignal quality, and thereby indicates to the base station which beamdirection to use in communicating with the user device.

Optionally, at 1515, the user device can also indicate in the feedbackmessage when two of the test signals provided similar high signalquality, and can thereby obtain improved angular resolution.

As a further option, the user device, after determining the angle towardthe base station according to the test signals in the first symbol-timeof the SSB message, may then be able to receive the remaining symbols ofthe SSB using a reception beam aimed at the base station, and therebyobtain substantially better signal and noise during the important PBCHand SSS portions of the SSB message. The user device can then obtain thesame advantages while receiving the SIB1 system information message andother system information messages, even before logging on to thenetwork.

If the user device does not have beamforming capability at 1516, thetask is done at 1517. However, if the user device does have beamformingcapability, and if the SSB message is followed by some identicalsignals, the user device can measure 1518 those identical signals usingvarious reception beams, and thereby determine which beam angle the userdevice can use for communicating with the base station.

As a further option, the user device can determine, according to whichtest signal is best received, an angle from the user device toward thebase station. This relies on the user device knowing which test signalis transmitted in which direction. For example, a formula may relate thesubcarrier of each test signal with the direction it is transmitted in.Then the user device can add or subtract 180 degrees to the angle of thebest-received test signal, and thereby determine the direction of thebase station.

FIG. 15C is a schematic showing another exemplary embodiment of a systeminformation message including multiple angular transmissions, accordingto some embodiments. As depicted in this non-limiting example, amodified SSB 1520 spans 5 or 6 symbol-times with a bandwidth of about1.9 MHz, substantially smaller than the configuration of the previousexample. The last symbol-time includes a large number ofseparately-aimed test signals 1521 for beam alignment, along with thelatitude and longitude of the base station. Optionally (not shown) theSSB 1520 can include further PBCH data or other data that the userdevice may need in one of the extra symbol-times.

As a further option, the base station may transmit the test signalsfirst, in a special symbol before the PSS. The user device could thenalign its reception beam toward the base station for receiving theremaining symbols of the SSB.

As a further option, the base station may transmit the test signals onthe PBCH periodically, in a symbol-time not connected to the SSBtransmissions. The user device can then detect the test signals,determine the direction toward the base station, and then use acorresponding reception beam for improved reception of the subsequentSSB and other system information messages, before making contact.

The user device can measure the amplitudes of the various test signals1521 as-received, and can then indicate the favored beam direction inone of the initial access messages, such as the preamble or the MsgA ofa two-step procedure. As mentioned, the test signals 1521, coveringmultiple angles around 360 degrees, may thereby enable the base stationto use the optimal downlink beam angle right from the beginning of theinitial access procedure. Also, if the user device already knows whichangle corresponds to which of the test signals, relative to geographicalnorth, and also has an electronic compass or the like, then the userdevice may transmit the preamble and other initial access messagestoward the base station in a directional uplink beam, further improvingthe access process.

The following examples show how feedback can be incorporated in theuplink and downlink messages of an initial access procedure.

FIG. 16A is a schematic showing an exemplary embodiment of messages forinitial access, according to some embodiments. As depicted in thisnon-limiting example, a new user seeking entry into the cell of a basestation can initiate the entry process by first finding and receivingthe SSB system information message broadcast by the base station. In theprior art, the base station transmits the SSB message multiple times ina variety of directions, each transmission including a sequence code.The user device determines which SSB transmission results in the bestsignal strength, and then indicates the associated sequence code duringinitiation, thereby informing the base station of the favored downlinkbeam for that user device. However, this requires that the base stationconsume a large amount of resources to transmit the heavy SSB message(960 resource elements) multiple times in multiple directions. Theexample discloses a far more economical procedure for accomplishing thesame task.

In this example, the base station broadcasts the SSB messageomnidirectionally, instead of multiple copies in various directions. Thetest signals of the previous example are not used in this example.Instead, in this example, the user devices determines the downlink beamdirection using feedback during the initial access messages. The initialaccess tasks are listed on the left, and possible message formats areshown for each action on the right. The SSB and SIB1 are assumed to havebeen transmitted omnidirectionally (without the test signals of theprevious example) and have been received by the user device. The userdevice than seeks beam alignment during the access message process asfollows.

At 1601, the user device transmits a random-access preamble 1606 on therandom-access channel, which the base station receives. Optionally, theuser device can append an alignment request 1607 to the preamble 1606.Preferably the alignment request 1607 is modulated in a modulationscheme that permits ready demodulation without a demodulation reference,such as 9QAM.

At 1602, the base station transmits a random access response (RAR) 1608message on the PDSCH, plus some number of trailing test signals 1609.(Here and elsewhere, blank resource elements are represented byhalf-height boxes.) The test signals 1609 are configured as short-formsingle-element demodulation references in this example, and aretransmitted in four different directions spaced by 90 degrees, asindicated by θ₁, etc. The user device then receives the test signals1609 and determines which one provides the best reception. At 1603, theuser device transmits “Msg3” 1611 of the RACH sequence on PUSCH,followed by feedback “fb” 1612, which in this example includes a beamselection indicating which of the four test signals 1609 was bestreceived. The feedback 1612 includes sufficient bits to specify thefavored beam from among the test signals 1609. Optionally, the feedback1612 may further include bits indicating which overlapping regions arealso received. Indicating the overlap regions may provide improvedangular resolution, as described later in the disclosure.

At 1604, the base station transmits “Msg4” 1614 on PDSCH, followed bythree more test signals 1615, 1616, 1617. The message 1614 and one ofthe test signals 1615 are transmitted in the previously-selected beamdirection θ. In addition, further test signals are transmitted atincrementally higher and lower angles θ+ and θ− labeled 1616, 1617. Theuser device can then transmit a multiplexed acknowledgement on PUCCH,with feedback at 1605 including the ACK/NACK multiplexed with anoptional scheduling request SR 1618, followed by a feedback selection1619 indicating both power and beam direction feedback. Thus the userdevice has selected a rough beam direction, fine-tuned that beamdirection, and requested a power adjustment, at the expense of just 8 or9 resource elements beyond those required for prior-art accessprocedures. More importantly, the network has eliminated many thousandsof unnecessary resource element transmissions of redundant directionalcopies of the SSB message, since the directional information is nowsupplied by test signals and feedback messages appended to the otheraccess messages, at negligible resource cost.

FIG. 16B is a schematic showing an exemplary embodiment of messages fora user-initiated beam and power adjustment procedure, according to someembodiments. As depicted in this non-limiting example, a user device canrequest a beam and power adjustment service from the base station. In afirst embodiment, the request, the beam test signals, and the feedbackmessage are all transmitted grant-free on a contention-based channel,such as the random access channel or another channel allocated forgrant-free transmissions. In a second embodiment, the user device cantransmit a scheduling request, obtain an uplink grant, and then use thatgrant for the alignment request instead of a BSR message. In a thirdembodiment, the user device can transmit a scheduling request, get a BSRgrant, transmit a BSR on the BSR grant, receive a message grant, andthen transmit the alignment request message using the message grant. Ina fourth embodiment, the user device can transmit the alignment requestinstead of the scheduling request, and the base station can then replywith a grant that includes three test signals aimed at three differentdirections, from which the user device can select the best reception,and can transmit the multiplexed power and beam feedback message on theresources provided by the grant. In the depicted example, the firstembodiment is assumed, and communications are bidirectional TDD on thecontention channel, in which each message may be time-spanning orfrequency-spanning.

For the contention-based version, at 1631 a user device can transmit analignment request or “ping request” on the contention channel. Thedepicted ping request here includes the C-RNTI identification 1635 ofthe user device, optionally preceded by a demodulation reference “ref”1634. The ping request may be preceded and followed by silent resourceelements as shown, to demark the message and enable a measure ofinterference.

At 1632, the base station transmits an alignment message “ping”consisting of three test signals, configured as demodulation references1636, 1638, 1639 transmitted at three angles. In this example, theangles are, first, the current beam direction previously established fordownlink messages to the user device, followed by the same plus andminus angular increments, that is, two beams incrementally left andright of the first direction. The alignment message may be transmittedfrequency-spanning (in one symbol-time) or time-spanning (on multiplesymbol-times) depending on the capabilities of the base station. Thealignment message is transmitted on the contention channel in this case,but in other embodiments it may be transmitted in downlink scheduledintervals, as mentioned. If the alignment message is to be transmittedat a pre-arranged interval following the request message, then the userdevice may be expecting the three test signals 1636, 1638, 1639 at thattime. On the other hand, if there is no pre-arranged transmission time(or if the alignment message is delayed by cross traffic, for example),then the base station can transmit the alignment message at a later timealong with the C-RNTI 1637 of the user device. Thus the user device canget the ping and test signals as soon as the channel is clear.

At 1633, the user device can reply, after a pre-arranged delay, with amultiplexed acknowledgement that also includes an SR scheduling requestif desired, along with power and beam angle feedback 1640, 1641 aspreviously described. Alternatively, also shown, the user device cantransmit its feedback data 1648, 1649 along with its identification code1647 at a later time, if the reply is delayed by cross-traffic or otherdelay. If necessary, the user device can also include a demodulationreference 1646.

As an option, the user device may submit the alignment request on thecontention-based channel, and then receive the alignment message ofthree test signals on another channel such as the PDCCH or PDSCH. Theuser device can then transmit the feedback message on the PUSCH orPUCCH, or it can continue transmitting on the contention channel,depending on the capabilities of the user device.

Thus the user device can provide feedback on power and beams wheneverthe received signal quality drops below a predetermined threshold, at acost of just a few resource elements.

FIG. 17A is a schematic showing an exemplary embodiment of messages forinitial access with user beam alignment, according to some embodiments.As depicted in this non-limiting example, a user device that hasbeamforming capability provides test signals during an initial accessprocedure and the base station provides feedback, so that the userdevice can align its transmission and reception beam toward the basestation. This is opposite to the case of FIG. 16A in which the basestation provided the test signals and the user device sent the feedbackmessage.

At 1751, the user device transmits a random access preamble 1756 on therandom access channel, followed by four overlapping beam signals atvarious angles preferably covering 360 degrees. In some cases, the userdevice may not be able to transmit multiple beams in differentdirections at the same time. In that case, the user device can transmitthe preamble 1756 frequency-spanning as usual, and then switch totime-spanning for transmission of the test signals 1757 sequentially.That is, the preamble 1756 may be transmitted in a single symbol-time,while the test signals 1757 may be transmitted in sequentialsymbol-times. The switch from frequency-spanning to time-spanning, ifneeded, is indicated by a gap 1758.

The test signals 1757 are wide-beam transmissions in this case, eachbeam spanning 135-degrees, spaced at 90 degrees around the full circle.Thus the wide beams are overlapped with neighboring beams, with45-degree overlap regions overlapping each neighboring beam. The fourbeams thereby define 8 sections (“octants”) covering 360 degrees, eachoctant being identified by which beam or beam-pair is best received. Theuser device can determine its octant by measuring the amplitude orsignal quality from each of the four test signals 1757, as described inmore detail below.

At 1752, the base station transmits the RAR (random access response)message 1759 followed by a feedback message 1760. The feedback message1760 may occupy a single resource element, and may be modulated toindicate which of the eight (beam or beam-pair) sections provides thebest signal quality or amplitude. For example, 8QAM or 9QAM can providethe necessary bits in a single resource element, without the need forabsolute amplitude calibration. Alternatively, the feedback message maybe transmitted using the 8-state or 9-state amplitude-phase modulationscheme instead, thereby obtaining improved noise margins at noadditional cost.

At 1753, the user device transmits a “Msg3” 1761 message on the PUSCHchannel, followed by three fine-tuning test signals 1762, directed atthree different angles within the selected quadrant or octant asdetermined by the previous feedback message. For example, each testsignal may be transmitted with a beam width of 15 degrees, arranged tofill the octant that was selected in the previous step, thereby enablingthe base station to select the best beam direction with a resolution of15 degrees (or 7.5 degrees if the overlap regions are also counted). Thethree test signals 1762 may be transmitted frequency-spanning ortime-spanning, depending on the user device's antenna capabilities.

At 1754, the base station receives the test signals 1762, determineswhich one, or which pair, provides the best signal, and transmits “Msg4”1764 along with a feedback message 1765 on the PDSCH channel. Thefeedback 1765 in this case is an incremental power adjustment request,multiplexed with a beam selection among the fine-tuning test signals1762, as discussed above.

At 1755, the user device transmits an acknowledgement multiplexed with ascheduling request 1768 on the PUCCH. Thus the user device has enabledthe base station to adjust the downlink beam direction and fine-tune it,and has also provided incremental feedback regarding the transmissionpower using the final downlink beam configuration, and has alsosubmitted a scheduling request if desired, with minimal expense ofresources.

FIG. 17B is a schematic showing an exemplary embodiment of messages fora base-initiated beam and power adjustment procedure, according to someembodiments. As depicted in this non-limiting example, a base stationcan provide beam test signals along with a grant, and the user devicecan respond with a multiplexed feedback at the granted time.

At 1771, the base station determines that the downlink beam to the userdevice needs to be refreshed, and therefore transmits a PDCCH message1774 which in this case includes the identification of the user deviceand a grant for the user device to reply. The message 1774 also includesa leading beam test signal 1773 which may be a short-form single-elementdemodulation reference, and two trailing beam test signals atincremental angles relative to the leading signal 1773.

At 1772, the user device can respond by transmitting an acknowledgementincluding multiplexed feedback on the PUCCH. In one version, theacknowledgement includes a multiplexed ACK/NACK with a SR request 1775,followed by a power and beam adjustment request 1776 based on the beamtest signals 1773.

In another version, also shown, the user device includes an optionaldemodulation reference 1777 and/or an identification code C-RNTI 1778with the acknowledgement/SR feedback 1779 and the power/beams feedback1780, to indicate which user is involved, or other information such as arequested change in the modulation and coding scheme for example.

In summary, the examples show how a user device can assist the basestation in aligning the base station's beam using test signals embeddedin the SSB message or added to various RACH messages, or subsequentlyprovided as part of a ping procedure initiated by either the user deviceor the base station, resulting in rapid and efficient alignment veryearly in the access procedure, with low to negligible costs in resourcesand transmitted energy.

Directionality Beams

The following examples show how beams can be configured in variousdirections to define angular sectors efficiently.

FIG. 18A is a schematic showing an exemplary embodiment of three angularregions, according to some embodiments. As depicted in this non-limitingexample, a base station can transmit three test signals in threedirections separated by 120 degrees, and can thereby reach user devicesin each section 1801 within an angular zone 1802. The user devices canthen respond with a feedback message, indicating which beam test signalwas best received. The feedback may be a multiplexed ACK/SR/power/beamfeedback message indicating which of the sections 1801 provided the bestsignal quality, as discussed. The base station can then transmit furtherdownlink messages to the user device in the selected section 1801.

FIG. 18B is a schematic showing an exemplary embodiment of threewide-angle beams, according to some embodiments. As depicted in thisnon-limiting example, a beam profile 1811 approximately matches the120-degree section 1801 of the previous example. The base station 1815can configure its antenna according to one of the beam profiles 1811,and can thereby deliver messages to user devices encompassed by theselected section 1811.

FIG. 18C is a schematic showing an exemplary embodiment of six regionsdefined by three overlapping beams, according to some embodiments. Asdepicted in this non-limiting example, six sections 1821, 1822 areindicated, each section 1821, 1822 spanning 60 degrees, yet they can beidentified using just three transmission beams. Each transmission beamis 180 degrees wide, as indicated 1823. Hence there are three overlapregions 1822 in which two of the beams overlap, and three non-overlapregions 1821 in which one beam predominates. The user device measuresthree test signals, one for each of the wide beams, and therebydetermines whether the best reception is obtained on just one of thetest signals or on two of the test signals with nearly equal amplitude.

Thus the user device determines whether one of the three 180-degree-widebeams is received with highest signal quality, or whether two of thebeams provide roughly comparable high signal quality. The sections instipple 1822 represent overlap regions in which two of the beams providesimilar high signal quality, whereas the undecorated sections 1821represent regions in which only one of the three beams prevails. Theuser device can then indicate, in a feedback message that includes atleast 6 options, which of the sections 1821, 1822 provided the bestsignal quality, and can thereby determine the correct downlink anglewithin 60 degrees.

FIG. 18D is a schematic showing an exemplary embodiment of threepartially-overlapping beams that define six regions, according to someembodiments. As depicted in this non-limiting example, three180-degree-wide transmission beams 1833 (shown in three different linetypes for clarity) are transmitted by a base station 1835 in threesequential resource elements. A user device can receive the transmissionbeams 1833 and measure the signal quality for each beam. The user devicecan then determine which section it occupies. The location determinationis based on which of the beams or beam pairs provides the best signalquality as-received. For example, if the user device is in a firstsection 1831, it will determine that the first transmitted beam providesthe best beam quality, and the other two beams provide much lower beamquality or none at all. If, however, the user device is in an overlapsection 1832, the user device will determine that two of the beamsprovide high and comparable signal quality. The user device can thentransmit a feedback signal indicating which of six sections the userdevice is in, based on the signal quality measurements. Thus with threepartially-overlapping wide beams, the user device can determine theoptimal beam direction within 60 degrees.

FIG. 19A is a schematic showing an exemplary embodiment of eight regionsdefined by four overlapping beams, according to some embodiments. Asdepicted in this non-limiting example, eight octant sections 1901, 1902,1903 are each 45 degrees wide. The sections are generated by fouroverlapping transmission beams each 135 degrees wide, as indicated bycurvy arrows 1904. A user device can determine which section it is in,by measuring the signal quality for each of the four transmission beams1904. If the user device determines that one of the four beams producesa signal quality substantially above all the other beams, then the userdevice can determine which of the non-overlapping sections 1901 it isin, labeled as θ₁, etc. However, if the user device determines that twoof the beams have similar high signal quality, then the user device isin one of the stippled overlap sections 1902 labeled as θ₁+θ₂, etc. Thuswith just four shaped partially-overlapping transmission beams, the userdevice can determine its direction within 45 degrees.

FIG. 19B is a schematic showing an exemplary embodiment of fourpartially-overlapping beams that define eight angular regions around abase station 1915, according to some embodiments. As depicted in thisnon-limiting example, four shaped 135-degree-wide transmission beams1914 are shown partially overlapping by 45 degrees (each beam is drawnin a different line type, for clarity). The beams 1914 produce bothoverlapping 1912 and non-overlapping 1911 regions, thereby generatingthe eight sections of the previous figure. The user device can therebyindicate whether a single test signal provided the best signal quality,or two adjacent beams provided similar high signal quality, and therebydetermine its angle to within about 45 degrees.

FIG. 19C is a schematic showing an exemplary embodiment of seven angularregions defined by partially overlapping narrow beams, according to someembodiments. As depicted in this non-limiting example, a user devicedetermines that it is in the octant section marked 1902 or 1912 of theprevious examples. The user device then receives four additionalpartially-overlapping beams which define seven sub-sections 1921, 1922.The seven sub-sections include four non-overlapping sub-sections 1921,alternating with overlapping (stippled) sub-sections 1922. A user devicein one of the non-overlapping sub-sections 1921 determines that the bestsignal quality corresponds to one of the beams, whereas a user device inone of the overlapping sub-sections 1922 determines that two of thebeams provide about the same high signal quality. Therefore, the userdevice can determine which sub-section it is in by comparing the signalquality for each of the four beams, and determining whether the signalquality for one of them is substantially better than any of the otherbeams, and if not, which two of the partially-overlapping beams provideabout the same high signal quality. The user device can therebydetermine its sub-section, with an angular resolution of about 6 or 7degrees in this case.

FIG. 19D is a schematic showing an exemplary embodiment of fourpartially-overlapping beams, according to some embodiments. As depictedin this non-limiting example, four 19-degree partially-overlappingtransmission beams 1931 are configured to produce regions wherein thesignal is dominated by one of the beams, alternating with regionswherein two of the beams produce about equal signals. Hence a userdevice can measure the signal quality for each of four test signals,determine whether high signal quality is provided in just one of thetest signals, or whether two of the test signals provide comparable highsignal quality. The user device can thereby indicate which sub-sectionit is in.

FIG. 20A is a schematic showing an exemplary embodiment of a messagewith multiple beam-test signals transmitted in different directions,according to some embodiments. As depicted in this non-limiting example,a message 2041 may include, in this case, four test signals 2042, suchas demodulation references, transmitted in various directions, asindicated by θ₁ etc. For example, the various beams 1914 as in FIG. 19B,or the narrower fine-tuning beams 1931 as in FIG. 19D, may be used fortransmitting the test signals 2042. The user device can then indicatewhich of the beams, or their overlap regions, provides the best signalquality. In the depicted embodiment, the four test signals 2042 areprovided at the end of the message 2041. In another embodiment, one ortwo of the test signals 2042 may be placed before the message 2041. Oneof the test signals 2042 may be transmitted in the same transmissionbeam as the message 2041 and therefore can be used for calibrating themodulation levels and demodulating the message. The other test signals2042 can serve as phase-tracking references, for enhanced phase-noiserejection.

FIG. 20B is a constellation chart showing an exemplary embodiment of afeedback procedure to select one of eight angular directions, accordingto some embodiments. As depicted in this non-limiting example, eightstates 2051 of 8QAM are shown. Each state 2051 can represent one of thesections of FIG. 19A, or one of the subsections of FIG. 19C, and canthereby enable the user device to select the beam direction or overlapthat provides the best signal quality, using just a single modulatedresource element for the feedback.

The central zero state 2052 is not needed in this example. In otherembodiments, requiring nine selection choices, the zero state 2052 maybe used.

Suitable for Standards

Due to the many options and variations disclosed herein, and otherversions derived therefrom by artisans after reading this disclosure, itwould be helpful for a wireless standards committee to establishconventions governing formats and implementation options for efficientfeedback procedures, such as those disclosed. Beneficial realtime beamadjustment and power adjustment, and corresponding feedback procedures,may enable users to communicate in 5G and 6G multi-GHz bands withincreased reliability, while avoiding unnecessary signaling and delays.

The wireless embodiments of this disclosure may be aptly suited forcloud backup protection, according to some embodiments. Furthermore, thecloud backup can be provided cyber-security, such as blockchain, to lockor protect data, thereby preventing malevolent actors from makingchanges. The cyber-security may thereby avoid changes that, in someapplications, could result in hazards including lethal hazards, such asin applications related to traffic safety, electric grid management, lawenforcement, or national security.

In some embodiments, non-transitory computer-readable media may includeinstructions that, when executed by a computing environment, cause amethod to be performed, the method according to the principles disclosedherein. In some embodiments, the instructions (such as software orfirmware) may be upgradable or updatable, to provide additionalcapabilities and/or to fix errors and/or to remove securityvulnerabilities, among many other reasons for updating software. In someembodiments, the updates may be provided monthly, quarterly, annually,every 2 or 3 or 4 years, or upon other interval, or at the convenienceof the owner, for example. In some embodiments, the updates (especiallyupdates providing added capabilities) may be provided on a fee basis.The intent of the updates may be to cause the updated software toperform better than previously, and to thereby provide additional usersatisfaction.

The systems and methods may be fully implemented in any number ofcomputing devices. Typically, instructions are laid out on computerreadable media, generally non-transitory, and these instructions aresufficient to allow a processor in the computing device to implement themethod of the invention. The computer readable medium may be a harddrive or solid state storage having instructions that, when run, orsooner, are loaded into random access memory. Inputs to the application,e.g., from the plurality of users or from any one user, may be by anynumber of appropriate computer input devices. For example, users mayemploy vehicular controls, as well as a keyboard, mouse, touchscreen,joystick, trackpad, other pointing device, or any other such computerinput device to input data relevant to the calculations. Data may alsobe input by way of one or more sensors on the robot, an inserted memorychip, hard drive, flash drives, flash memory, optical media, magneticmedia, or any other type of file-storing medium. The outputs may bedelivered to a user by way of signals transmitted to robot steering andthrottle controls, a video graphics card or integrated graphics chipsetcoupled to a display that may be seen by a user. Given this teaching,any number of other tangible outputs will also be understood to becontemplated by the invention. For example, outputs may be stored on amemory chip, hard drive, flash drives, flash memory, optical media,magnetic media, or any other type of output. It should also be notedthat the invention may be implemented on any number of different typesof computing devices, e.g., embedded systems and processors, personalcomputers, laptop computers, notebook computers, net book computers,handheld computers, personal digital assistants, mobile phones, smartphones, tablet computers, and also on devices specifically designed forthese purpose. In one implementation, a user of a smart phone orWi-Fi-connected device downloads a copy of the application to theirdevice from a server using a wireless Internet connection. Anappropriate authentication procedure and secure transaction process mayprovide for payment to be made to the seller. The application maydownload over the mobile connection, or over the Wi-Fi or other wirelessnetwork connection. The application may then be run by the user. Such anetworked system may provide a suitable computing environment for animplementation in which a plurality of users provide separate inputs tothe system and method.

It is to be understood that the foregoing description is not adefinition of the invention but is a description of one or morepreferred exemplary embodiments of the invention. The invention is notlimited to the particular embodiments(s) disclosed herein, but rather isdefined solely by the claims below. Furthermore, the statementscontained in the foregoing description relate to particular embodimentsand are not to be construed as limitations on the scope of the inventionor on the definition of terms used in the claims, except where a term orphrase is expressly defined above. Various other embodiments and variouschanges and modifications to the disclosed embodiment(s) will becomeapparent to those skilled in the art. For example, the specificcombination and order of steps is just one possibility, as the presentmethod may include a combination of steps that has fewer, greater, ordifferent steps than that shown here. All such other embodiments,changes, and modifications are intended to come within the scope of theappended claims.

As used in this specification and claims, the terms “for example”,“e.g.”, “for instance”, “such as”, and “like” and the terms“comprising”, “having”, “including”, and their other verb forms, whenused in conjunction with a listing of one or more components or otheritems, are each to be construed as open-ended, meaning that the listingis not to be considered as excluding other additional components oritems. Other terms are to be construed using their broadest reasonablemeaning unless they are used in a context that requires a differentinterpretation.

1. A method for a base station of a wireless network to use an artificial intelligence model to adjust a transmission beam parameter, the method comprising: a) transmitting a downlink message concatenated with a first test signal and a second test signal; b) wherein the first test signal is transmitted according to a first setting of the transmission beam parameter, the second test signal is transmitted according to a second setting, different from the first setting, of the transmission beam parameter, and the downlink message is transmitted according to a third setting, different from the first and second settings, of the transmission beam parameter; c) receiving, from a user device, a feedback message indicating whether the downlink message, or the first test signal, or the second test signal was best received; d) providing, as input to the artificial intelligence model, the first, second, and third settings of the transmission beam parameter; e) providing, as further input to the artificial intelligence model, the feedback message or a digest thereof; and f) determining, as output from the artificial intelligence model, an adjustment of the transmission beam parameter.
 2. The method of claim 1, wherein the downlink message is transmitted according to 5G or 6G technology.
 3. The method of claim 1, wherein the transmission beam parameter is a direction of a transmission beam.
 4. The method of claim 1, wherein the transmission beam parameter is a width of a transmission beam.
 5. The method of claim 1, wherein the transmission beam parameter is a frequency of a transmission beam.
 6. The method of claim 1, wherein the first setting is larger than the third setting and the second setting is smaller than the third setting.
 7. The method of claim 1, wherein the feedback message is concatenated with or multiplexed with an acknowledgement message.
 8. The method of claim 1, wherein the feedback message is concatenated with or multiplexed with a request to adjust a transmission power level.
 9. The method of claim 1, further comprising: a) providing, as further input to the artificial intelligence model, data about the user device and data about the third setting of the transmission beam parameter; and b) determining, as further output from the artificial intelligence model, suggested values for the first and second settings of the transmission beam parameter.
 10. Non-transitory computer-readable media containing an artificial intelligence model and instructions that, when executed by a computing environment, cause a method to be performed, the method comprising: a) providing, as input to the artificial intelligence model, data about a wireless network, data about a particular user device of the wireless network, and data about a background interference level in the wireless network; b) determining, as output from the artificial intelligence model, a particular setting of a parameter of a downlink transmission beam for communicating with the particular user device; and c) transmitting a downlink message to the particular user device, the downlink message transmitted according to the particular setting of the parameter of the downlink transmission beam.
 11. The non-transitory computer-readable media of claim 10, wherein the data about the wireless network comprises: a) a number of active user devices in the wireless network; and b) a spatial distribution of the active user devices in the wireless network.
 12. The non-transitory computer-readable media of claim 10, wherein the data about the particular user device comprises: a) a capability of the particular user device, the capability comprising at least one of: i) an ability to transmit using a directed transmission beam; or ii) an ability to receive using a directed reception beam; and b) a location of the particular user device, the location comprising at least one of: i) a distance of the particular user device from the base station; or ii) an angle of the particular user device relative to the base station.
 13. The non-transitory computer-readable media of claim 10, wherein the data about the background interference level in the wireless network comprises at least one of: a) a current amplitude or power level of radio-frequency signals arriving at the base station from outside the wireless network; and b) a current amplitude or power level of radio-frequency signals arriving at the particular user device from outside the wireless network.
 14. The non-transitory computer-readable media of claim 10, wherein the parameter of the downlink transmission beam comprises at least one of: a) a direction of the downlink transmission beam; b) a width of the downlink transmission beam; and c) a power level of the downlink transmission beam.
 15. The non-transitory computer-readable media of claim 10, wherein: a) the downlink message is concatenated with at least two test signals, wherein: i) a first test signal of the two test signals is transmitting according to a first setting, higher than the particular setting, of the parameter of the downlink transmission beam; and ii) a second test signal of the two test signals is transmitting according to a second setting, lower than the particular setting, of the parameter of the downlink transmission beam.
 16. A method for a wireless base station or core network to manage a wireless network, the method comprising: a) determining an initial value of a performance metric of the wireless network; b) providing, as input to an artificial intelligence model, data about active user devices of the wireless network; c) providing, as further input to the artificial intelligence model, data about current operating conditions of the wireless network; d) determining, as output from the artificial intelligence model, one or more suggested adjustments of one or more operating parameters of the wireless network; e) adjusting the one or more operating parameters according to the one or more suggested adjustments; and f) determining whether the performance metric of the wireless network has increased or decreased or remained unchanged.
 17. The method of claim 16, wherein the data about active user devices of the wireless network comprises: a) a number and a spatial distribution of the active user devices of the wireless network; and b) data about capabilities of the active user devices of the wireless network.
 18. The method of claim 16, wherein the data about current operating conditions of the wireless network comprises: a) a current message throughput in the wireless network; and b) a current message delay or failure rate of the wireless network.
 19. The method of claim 16, wherein the one or more operating parameters comprise at least a maximum uplink transmission power limit for user device transmissions, and a maximum downlink transmission power limit for base station transmissions.
 20. The method of claim 16, wherein the adjusting the one or more operating parameters is performed automatically by a transmitter of the base station, responsive to commands or signals from the artificial intelligence model. 